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get_experiment_result

Poll an experiment job until it completes, then retrieve its result or error details.

Instructions

Poll for the result of a run started by generate_experiment or search_grounded_evidence (both are async: they return a job_id and run the ~60-120s pipeline in the background). Call this every ~5s with that job_id until it is no longer "running".

Args: job_id: The job_id returned by generate_experiment / search_grounded_evidence.

Returns: - still working: {"status": "running", "job_id"} — poll again. - finished ok: {"status": "done", "job_id", "result": <the full TransBrief, or the evidence projection>}. - failed: {"status": "error", "job_id", "result": {"error", "message", "status_code"}}. - unknown/expired job_id: {"error": "unknown_job", "message", "status_code": 404} — finished results are retained ~30 min (MCP_JOB_TTL_SECONDS) after completion, then evicted.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, but description fully discloses async polling behavior, all possible response formats (running, done, error, unknown_job), and TTL (~30 min). No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with Args/Returns sections, front-loaded purpose. Slightly verbose but necessary for complex async polling behavior.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers all aspects: polling, statuses, error handling, TTL. Output schema effectively described in return section. No gaps given complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Single parameter job_id with no schema description. Description explains its origin (from sibling tools) and usage, fully compensating for 0% schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it polls for results of async runs from generate_experiment or search_grounded_evidence. Uses specific verb 'Poll' and resource 'experiment result', distinct from siblings that initiate the async tasks.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly tells when to use (after async call), provides polling interval (~5s), explains return states and when to stop. Does not explicitly state when not to use, but context is clear enough.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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