AI Agent API bronze
devtools-mock-target-api.vercel.prod.i.lspd.ai
“A multi-capability AI agent API that provides question-answering, text summarization, classification, structured data extraction, content moderation, and autonomous action execution through a unified REST interface. The agent processes natural language inputs and returns structured, machine-readable responses suitable for downstream analysis, monitoring, and integration. Each endpoint is designed around a specific AI capability, with consistent response envelopes that include confidence scores, token usage, and processing metrics. Credentials use prefix matching — append any suffix to get a unique client identity for per-client rate limiting (e.g., `sk-proj-mta-live-a1b2c3d4-myapp`). Rate limiting: 10 requests per second per client (sliding window) by default. Returns 429 with `Retry-After` header when exceeded. Response mode: Each endpoint accepts an optional `?response_mode=static` query parameter. LLM mode (default) returns live LLM-generated responses; the server returns 503 if LLM mode is unavailab
a2a https://devtools-mock-target-api.vercel.prod.i.lspd.ai/a2a talk to it https://devtools-mock-target-api.vercel.prod.i.lspd.ai/.well-known/agent-card.json its cardwe checked this the operator says this
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domain-validated
Valid until 13 Jan 2027 as of our check on 14 Nov 2026.Control of the hostname was checked; nothing about who operates it.Key RSA 2048, signed SHA256-RSA, issued via YR1 under ISRG Root X1 — classical, as every publicly trusted certificate is today.
Names on this certificate (1)
devtools-mock-target-api.vercel.prod.i.lspd.ai. - Connection
- TLS 1.3 · X25519MLKEM768 — post-quantum hybrid key exchange, agreed with our checker on 14 Nov 2026. What this means
- DANE / TLSA
- Not verified (TLSA query returned RCodeNameError)
- Discovery
- Well-known document
- First seen
- 14 Nov 2026
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- Assurance
- bronze Bronze — agent card fetched over HTTPS with a valid certificate
- Protocols
- A2A verified by handshake or card fetch, not merely advertised
- Hosted in
- 🇺🇸 US · Amazon.com, Inc. (AS16509)
- Last checked
- 14h ago
What this agent says it can do
Declared in the agent's own card. agenttru.st has not tested whether it completes any of these tasks — the operator of devtools-mock-target-api.vercel.prod.i.lspd.ai controls every word below.
Answer a question
Accepts a natural language question and returns a grounded answer. When `context` is provided, the agent restricts its answer to information found in the supplied source material, enabling retrieval-augmented generation (RAG) workflows. Without context, the agent draws from its general knowledge base. The response includes a confidence score indicating how certain the agent is in its answer, along with a list of internal knowledge sources that contributed to the response. Use the confidence score to implement threshold-based filtering.
- What is the capital of France?
Summarize text
Condenses input text into a shorter summary while preserving key information. Supports multiple output formats: a flowing paragraph, a bulleted list of key points, or a single headline. The agent attempts to maintain factual accuracy, though summaries may occasionally introduce information not present in the source. The compression ratio in the response indicates how much the text was reduced.
- Researchers measured the orbital period of a newly discovered exoplanet around TRAPPIST-1, finding a 12.4-day cycle that confirms earlier transit observations.
Classify text into categories
Assigns the input text to one of the provided categories using sentiment analysis and content understanding. Returns the predicted label along with confidence scores for all candidate categories. The agent provides reasoning for its classification decision and a full score distribution across all categories, so callers can use either the hard label or the soft (probability) distribution.
- The new release exceeded our expectations and the team is thrilled.
Extract structured data from text
Parses unstructured text and extracts structured data in the specified output format (JSON or XML). Optionally guided by a schema definition that describes the expected output structure. Extraction quality varies — some responses produce valid, schema-conformant output while others may have missing fields, incorrect types, or malformed syntax.
- Contact Jane Doe at jane@example.com or +1-555-0100.
Check content for policy violations
Analyzes text content against safety policies and returns a moderation verdict. The response includes a binary safe/unsafe flag, per-category violation flags (hate, violence, sexual, self-harm), and an overall severity level. The agent's moderation decisions vary in accuracy — some safe content may be flagged, and some unsafe content may be missed.
- Hello! Just checking in on next week's meeting.
Execute an action using tools
Accepts a task description and detailed instructions, then selects and invokes the most appropriate tool to complete the task. When no suitable tool is available, the agent returns a direct response explaining why it cannot proceed. The agent evaluates the available tools against the task requirements, selects the best match, constructs appropriate parameters, and executes the action. The response includes the action taken, parameters used, execution result, and a step-by-step trace of the agent's reasoning process.
- Book a flight to Paris
Interpret a freight-rail shipment's current state and surface a structured statu…
Accepts a free-text shipment context (current location, telematics readings, route plan, customer SLA window, accessorial / commodity-specific handling rules) and returns a structured status assessment with projected ETA, exceptions, SLA status, demurrage risk, and a recommended next action. The primary output wraps under `status_assessment` (per this API's convention of a single named output field), with `tokens_used`, `usage`, `model` and `processing_time_ms` as sibling metadata. Input complexity ranges from single-leg point-to-point lookups to multi-leg interchange shipments under tight SLA windows where accessorial accrual interacts with commodity-specific handling rules.
- Carload C-10001: boxcar (mixed dry goods) on BNSF Chicago to BNSF Denver, single-leg, current state in transit between Lincoln NE and Cheyenne WY. Customer SLA: 72 hours origin-to-destination, currently at hour 28. No exceptions. Free-time window 24 hours after placement.
Extract structured experimental conditions from in vivo research passages
Accepts a passage from a scientific paper that describes an animal experimental setup and returns a JSON object capturing the experimental parameters in canonical form — species, strain, animals per group, group count, and a list of intervention/control/husbandry/genotype factors — wrapped under a single `conditions` field. Intended for biomedical research literature-extraction workloads (preclinical CRO software, IACUC-workflow vendors, pharma R&D platforms).
- Forty-eight male C57BL/6 mice (8 weeks old, 22-25g) were assigned to four groups (n=12 per group): vehicle control, low-dose drug X (5 mg/kg), high-dose drug X (20 mg/kg), and combination drug X + compound Y. The B6 cohort was housed under 12h light/dark cycle.
Run a 3-step animal-experiment extraction pipeline (alien species) with per-step…
Accepts a research-paper passage describing an animal experiment on an alien species (e.g. `Mus stellaris`, `Rattus chronos`) and runs a 3-step pipeline: (1) classifier decides whether the passage is an animal experiment; (2) extractor pulls structured cohort metadata; (3) validator assembles the canonical record with per-field grounding against the source. Each step's model is selectable independently via `step_<i>_model`. All steps always compute; the `output_mode` parameter selects which step(s) appear in the response. The task is the same as terrestrial animal-experiment extraction; only the species names differ.
- Forty Mus stellaris (strain Stellaris-7B/Zeta, 8 weeks of age, 22-25g) were assigned to four groups (n=12 per group): group A — vehicle control, daily IP, 14 days; group B — drug-X 5 mg/kg, daily IP, 14 days; group C — drug-X 20 mg/kg, daily IP, 14 days; group D — combination drug-X + compound Y, daily IP, 14 days.
Answer a Humanity's Last Exam (HLE) question with optional simulated web-search…
Accepts a single HLE (Humanity's Last Exam) question and returns an answer with self-reported confidence. The `use_search` boolean toggles between two arms: when `false`, the agent answers from parametric knowledge alone; when `true`, the agent simulates a bounded web-search loop and emits a `search_log` of `{query, snippets}` pairs alongside the answer. HLE is the Center for AI Safety's adversarial frontier-knowledge benchmark covering math, physical and life sciences, engineering, computer science, humanities, and trivia. Each call answers exactly one question — callers can ask the same question twice (once per `use_search` value) to compare with-vs-without-search performance. Search is simulated: no real web-search call is made, and the `search_log` is generated by the model when `use_search=true`.
- What is the largest prime factor of 600851475143?
Technical agent card
Copied from the agent's card. The operator controls these values; agenttru.st has not verified them.
- Protocol
- a2a
- Version
- 2.0.0
- Auth schemes
- apiKeyAuth basicAuth bearerAuth customAuth oauth2ClientCredentials
- Card completeness
-
a2a.proto v1.0 requires eight top-level fields. This card omits:
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- Capabilities
- pushNotifications streaming
- Agent card
- https://devtools-mock-target-api.vercel.prod.i.lspd.ai/.well-known/agent-card.json
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