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similar_projects

Read-only

Find projects related by SEMANTIC similarity, using each project's objective (learning-goal) embeddings — falling back to its theme embeddings when objectives are not embedded yet. Pass a seedProjectId to rank the projects most similar to it (with 0–1 scores). Pass null for seedProjectId to cluster ALL of the org's projects into thematic groups. Each project notes its basis ("objectives" or "themes"). Use this for "which projects are like X?" or "group my projects"; use cross_project_comparison instead to compare a known set side-by-side by theme.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
maxProjectsNoSeed mode only: how many similar projects to return (default 5, max 10).
seedProjectIdNoA project id to rank others against. Pass null to cluster ALL the org's projects instead.
similarityThresholdNoCluster mode only: cosine cutoff to group projects (0.3–0.95, default 0.6). Higher = tighter, more clusters.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNo
noteNo
seedNo
basisNo
errorNo
similarNo
clustersNo
thresholdNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare it as a safe read (readOnlyHint=true, destructiveHint=false, openWorldHint=false), yet the description adds real behavioral detail beyond them: the objectives→themes fallback, the per-project `basis` marker, and the 0–1 score range. It does not discuss cost/latency of the all-projects clustering mode, so it falls short of a 5.

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?

Front-loaded with the core purpose, and every sentence carries a distinct fact (fallback, seed mode, cluster mode, basis field, sibling routing). It is dense—the second sentence packs several clauses—but nothing is wasted.

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?

An output schema exists, so return formatting need not be explained, and the description still covers both operating modes, the fallback logic, and the routing decision to a sibling. Nothing needed to call it correctly is missing.

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

Parameters3/5

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

Schema description coverage is 100%, so all three parameters are already documented including their mode restrictions and defaults. The description restates the seed/null distinction and the basis/score semantics rather than adding new syntax or constraints, which is the baseline-3 case.

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 (find/rank/cluster) and resource (projects) plus the mechanism (objective embeddings with theme-embedding fallback). It explicitly distinguishes itself from cross_project_comparison, so an agent can route between them without opening either 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?

Gives explicit dual-mode usage: pass a seedProjectId to rank similar projects, pass null to cluster the whole org, and names the alternative (cross_project_comparison) with the condition that selects it (compare a known set side-by-side by theme). Two concrete user phrasings are even supplied.

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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