Fetch a stored experiment in full: the recipe (settings, interventions, windows, spec_version), the summary and the complete measured series. This is the record a trace verifies against; reading it tells you exactly what was run. What would you change in the recipe to move the result?
| Name | Required | Description | Default |
|---|---|---|---|
| experiment_id | Yes | 21-char experiment id, as returned by experiment_run |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / experiment_id / descriptionAdded value: +"21-char experiment id, as returned by experiment_run"Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the behavioral burden. It conveys a read-only fetch and details what is returned, but it does not disclose error behavior, authentication needs, or explicitly confirm the absence of side effects beyond the word 'Fetch'.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The first two sentences are informative and front-loaded, but the final question—'What would you change in the recipe to move the result?'—is an analysis prompt rather than tool-call guidance. It does not earn its place in a tool definition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter read tool with no output schema, the description adequately explains what is returned and why the tool is useful. Minor omissions like error handling and exact response shape are acceptable, though the stray question adds unnecessary noise.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter, experiment_id, is already fully documented in the schema as a 21-char id returned by experiment_run, so schema coverage is 100%. The description adds no additional parameter-level meaning, keeping this at the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource ('Fetch a stored experiment in full') and enumerates the returned contents: recipe, summary, and measured series. It clearly identifies what the tool does, though it does not explicitly contrast it with sibling tools like experiment_run or trace_get.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives a clear use context: this is the record a trace verifies against, so an agent should call it to see exactly what was run. It implies the distinction from experiment_run, but it does not explicitly state when not to use it or name alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools are cleanly separated by resource prefix (flock_, experiment_, trace_, proposal_) and action, so an agent can usually tell them apart. The main ambiguity is between experiment_run/experiment_get and the flock_* session tools, since both drive the same four-weight simulation; the descriptions mitigate this by framing experiment_run as the one-call batch alternative.
The dominant pattern is <resource>_<action> with readable verbs like create, get, step, browse, submit, and leave, and each resource family is internally consistent. Minor outliers like playground_manifest and what_changed don't follow the verb_noun convention, and browse/get both appear for retrieval, so the naming is mostly consistent but not uniform.
At 15 tools the set sits at the upper edge of the normal range, and the breadth is justified by the playground's dual simulation/experiment and community (traces/proposals) surfaces. It isn't bloated, but the experiment_* pair partially overlaps the flock_* session workflow, making the set feel slightly larger than strictly necessary.
The main lifecycle is covered: run/get for experiments, create/step/get/set_params/timeline for flocks, browse/get/leave for traces, and browse/submit for proposals, with manifest and what_changed orienting returning agents. Minor gaps exist—no experiment or flock listing endpoint and no proposal detail/withdraw tool—but discovery is handled through traces and what_changed, so agents can still complete workflows.