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Glama

Wmata

by io.github.pipeworx-io

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Idempotent

Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesMemory key (e.g., "subject_property", "target_ticker", "user_preference")
valueYesValue to store (any text — findings, addresses, preferences, notes)

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Adds behavioral context beyond annotations: key-value pair scoped by identifier, persistence differences, and pairing with recall/forget. Annotations include idempotentHint and destructiveHint, which are consistent.

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?

Three sentences, front-loaded with purpose, then usage scenarios and technical details. No superfluous words; every sentence adds value.

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?

Given low complexity (2 params, no output schema), description covers purpose, when-to-use, persistence behavior, and sibling tools. Complete for agent understanding.

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 already covers both parameters with clear descriptions (key with example pattern, value as any text). Description does not add new semantic details beyond schema, so baseline 3 is appropriate.

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?

Description clearly states it saves data for later reuse across conversation/sessions, provides specific examples (ticker, address, preference), and distinguishes from siblings by mentioning recall and forget.

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 states when to use (when discovering something worth carrying forward) and provides examples. Also explains persistence differences for authenticated vs anonymous users and pairs with recall/forget for retrieval/deletion.

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

A3.7/5.0
Disambiguation3/5

Most tools have distinct purposes with detailed descriptions, but the server mixes WMATA transit tools (bus routes, rail predictions) with unrelated general-purpose tools (deep_research, polymarket_arbitrage). This broad scope can confuse an agent expecting a focused transit server.

Naming Consistency2/5

All names use snake_case, but there is no consistent verb_noun pattern. Many are noun_noun (bus_routes, rail_lines) or verb_properNoun (ask_pipeworx), and the naming conventions vary wildly across different domains (e.g., validate_claim vs entity_profile vs scan_dependency).

Tool Count2/5

41 tools is excessive for a WMATA transit server. The vast majority of tools (ask_pipeworx, deep_research, polymarket_*) are not related to WMATA, making the tool surface bloated and unfocused for its stated purpose.

Completeness3/5

The WMATA-specific tools cover core bus and rail operations (incidents, predictions, routes, stations). However, gaps like fare info and elevator status are missing. The non-transit tools are extensive but irrelevant to the server's apparent focus.

Resources