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Susurration

experiment_run

Run one bounded, reproducible experiment in a single call instead of many step calls: set the four weights, ticks (max 5000), a sample_every interval, optional interventions (weights that change at given ticks) and optional windows (tick ranges to summarise). Returns a compact summary plus an experiment_id; fetch the full measured series with experiment_get. The recipe is stored replay-verifiably, so a trace can cite the experiment_id and the server re-runs the whole recipe, interventions included, to verify it. The open question at trace wtcclksRGbcqGTAxCVEUw is a natural first use: where in (0.10, 0.12) does disorder start winning, and can you find it in a handful of calls instead of hundreds?

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nNonumber of birds (10 to 400, default 120)
seedNouint32 PRNG seed; omit for a random one. Same seed and recipe replay byte-identically
noiseNonoise weight (0 to 1): random heading jitter per tick; 0 is fully deterministic (spec v1 behaviour)
ticksYestotal ticks to simulate (1 to 5000)
windowsNotick ranges [start, end] to summarise separately (max 20)
cohesionNocohesion weight (0 to 1): pull toward the centre of nearby birds
alignmentNoalignment weight (0 to 1): steer toward the average heading of nearby birds
separationNoseparation weight (0 to 1): steer away from birds that come too close
sample_everyNomeasure metrics every N ticks (default 10)
interventionsNoweight changes applied mid-run (max 20 entries)
time_to_polarizationNopolarization threshold (0 to 1); the summary reports the first sampled tick at or above it

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and it does well: it discloses boundedness (ticks max 5000), reproducibility, replay-verifiable recipe storage with server-side re-execution, and the compact-summary-plus-experiment_id return shape. It doesn't discuss output details or side effects, but the key non-obvious replay-verification behavior is clearly stated.

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?

The first sentence front-loads the core behavior and parameter groups; the second is compact and names the return artifacts plus follow-up path; the third adds replay verification and a concrete use case. The open-question sentence is domain-specific and slightly long, but it earns its place as practical guidance.

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

Completeness4/5

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

For an 11-parameter tool with no output schema and no annotations, the description covers purpose, boundedness, reproducibility, return artifacts, and the follow-up tool. It doesn't detail the compact summary's fields or mention time_to_polarization/n/seed, but the schema covers those parameters and the essential invocation behavior is clear.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3; the description adds semantic grouping ('the four weights'), clarifies interventions as mid-run weight changes and windows as tick-range summarisers, and links sample_every to measurement cadence. It doesn't repeat every field, but the schema already documents them.

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?

States a specific verb ('Run') plus resource ('one bounded, reproducible experiment') and explicitly positions it as the batch alternative to many step calls. It names the returned artifacts and sibling experiment_get, making it distinguishable without opening the schema.

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

Usage Guidelines5/5

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

Explicitly says to use this instead of many step calls and routes the follow-up full-series retrieval to experiment_get. It also provides a concrete first-use scenario with a trace ID and a specific scientific question, which is strong contextual guidance.

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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TDQS

A3.8/5.0
Disambiguation4/5

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.

Naming Consistency4/5

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.

Tool Count4/5

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.

Completeness4/5

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.

Resources