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labs-catalog-mcp

by sudeeplalka-hash
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labs-catalog-mcp

A real MCP server, with a real captured session. LB-07 in the AI Labs portfolio's Live Builds collection: the portfolio's MCP Server Contract Workbench (GAP-01) simulates the wire protocol to teach it — this repo is the live counterpart a real client can connect to.

The server exposes the portfolio's own labs registry as tools:

tool

what it does

list_labs

list labs, filter by collection (0–5) or badge (LIVE/SIMULATED)

get_lab

one lab's problem, decision, status, route

search_decisions

find the lab for the enterprise decision you're facing

Run it

npm install
npm start            # stdio MCP server
npm run capture      # real client session -> session-transcript.jsonl + console output

Related MCP server: Enterprise MCP Gateway and Tool Registry

Connect Claude Desktop

{
  "mcpServers": {
    "labs-catalog": { "command": "node", "args": ["/absolute/path/to/server.mjs"] }
  }
}

The captured session

session-transcript.jsonl / session.md hold an actual recorded session — initialize handshake, tools/list, and three tool calls, both directions, timestamped. Recorded by tee-wire.mjs, a 30-line wire tap between client and server; re-run npm run capture and it regenerates. No frame in this repo is hand-written.

Honesty & attribution

Built on @modelcontextprotocol/sdk (MIT) and zod. Catalog data is a generated snapshot of the portfolio's packages/kit/src/registry.ts. Server, wire tap, and capture harness are original. MIT licensed.

Available Tools

3 tools
get_labB

Full detail for one lab: the problem it addresses, the decision it enables, status, and route.

ParametersJSON Schema
NameRequiredDescriptionDefault
idYes

TDQS

B3.3/5.0
Behavior2/5

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

No annotations provided. Description mentions output content but does not disclose behavioral traits like read-only nature, side effects, or permissions required. The burden is on the description, but it insufficiently addresses behavior.

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?

Single sentence, no clutter, front-loaded with key information. Could be slightly more efficient but overall concise.

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

Completeness3/5

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

Adequate for a simple tool with one parameter and no output schema, but lacks behavioral transparency and parameter details. Sufficient but not comprehensive.

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

Parameters2/5

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

Only one parameter 'id' with no schema description (0% coverage). The description implies id identifies a lab but adds no format or constraints beyond the schema.

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?

The description clearly states it retrieves full detail for one lab, listing specific aspects (problem, decision, status, route). It distinguishes from siblings: list_labs (list) and search_decisions (search).

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

Usage Guidelines3/5

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

Implied usage: use for detailed info on a single lab. No explicit guidance on when not to use or alternatives, but sibling names provide context.

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

list_labsA

List the portfolio's labs, optionally filtered by collection (0-5) or live mode (LIVE/SIMULATED).

ParametersJSON Schema
NameRequiredDescriptionDefault
liveNo
collectionNo

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations, the description carries full disclosure burden. It indicates a read-only operation ('List') but lacks details on pagination, ordering, or behavior when no labs match filters. Basic transparency is present.

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

Conciseness5/5

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

A single sentence that is concise, front-loaded, and contains no superfluous information. Every word serves a purpose.

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

Completeness3/5

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

Given no output schema and low complexity, the description covers the tool's purpose and parameters adequately but omits return format or pagination details. It leaves some uncertainty for the agent.

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 0%, so the description must explain parameters. It does so effectively: 'collection (0-5)' adds context beyond the integer schema, and 'live mode (LIVE/SIMULATED)' clarifies the enum values. Thus, it adds meaningful value.

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?

The description clearly states the action ('List') and resource ('portfolio's labs'), and distinguishes from siblings by specifying optional filtering. It is specific and unambiguous.

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

Usage Guidelines3/5

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

The description implies when to use (to list labs with optional filters) but does not explicitly contrast with siblings like 'get_lab' (single lab) or 'search_decisions' (different operation). No when-not-to-use guidance is given.

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

search_decisionsA

Search the catalog by the enterprise decision you are facing (matches problem/decision/title text).

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYes

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description must carry the behavioral burden. It indicates a search operation, which is inherently read-only, but does not disclose result format, pagination, ordering, or error behavior.

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

Conciseness5/5

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

A single sentence of 15 words, front-loaded with key information, no redundant or unnecessary words.

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

Completeness2/5

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

Given no output schema and simple tool, the description is incomplete. It does not describe what the search returns (e.g., list of decisions), which is crucial for an agent to invoke correctly.

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?

The schema has 0% description coverage, but the description adds meaning to the 'query' parameter by stating it matches problem/decision/title text. However, it lacks details on matching behavior (e.g., exact vs fuzzy) and does not cover other potential parameters.

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?

The description clearly states the verb 'search', the resource 'catalog of decisions', and specifies that it matches 'problem/decision/title text'. It distinguishes from sibling tools which are about labs.

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

Usage Guidelines3/5

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

No explicit guidance on when to use this tool versus alternatives. The description implies usage when searching for decisions, but doesn't provide when-not-to-use scenarios or context.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 3 tool updatesv1.0.0
    • First observedget_lab
    • First observedlist_labs
    • First observedsearch_decisions
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4/5.0

Scored across 3 tools

Disambiguation5/5

Each tool targets a distinct operation: retrieving full details of a single lab, listing labs with filters, and searching by decision text. There is no overlap in functionality.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (get_lab, list_labs, search_decisions), making the set predictable and easy to navigate.

Tool Count5/5

Three tools is an appropriate size for a focused catalog service, covering the primary use cases of detail retrieval, listing, and search without unnecessary bloat.

Completeness5/5

The tools provide full coverage for a read-only catalog: listing with filters, full detail retrieval, and full-text search. No obvious gaps exist for the stated purpose.

Maintenance

ActivityStale
ResponsivenessNo issues

Resources

Related MCP Connectors

Related MCP Servers