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Glama

Irail

by io.github.pipeworx-io

Discover Tools

discover_tools
Read-onlyIdempotent

Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed. Call this FIRST when you have many tools available and want to see the option set (not just one answer).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoAlias for query.
taskNoAlias for query.
limitNoMaximum number of tools to return (default 20, max 50)
queryYesNatural language description of what you want to do (e.g., "analyze housing market trends", "look up FDA drug approvals", "find trade data between countries"). Accepts task, q, description, search as aliases.
searchNoAlias for query.
descriptionNoAlias for query.

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?

Description adds detail beyond annotations: output includes full schemas with curated examples, ready to call directly. No contradictions with annotations. Explains what the return looks like.

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?

Two concise sentences. First sentence states purpose, second explains output and usage. Front-loaded and efficient.

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 complexity (many siblings, 6 parameters, no output schema), description provides complete context: return structure, example domains, and positioning as discovery entry point.

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 coverage is 100%; description does not add new semantics about parameters beyond listing example query strings. 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 the tool finds tools by describing data or task, lists many domains, and explains what it returns (top-N tools with schemas). Distinguishes itself from siblings by positioning as a first-call discovery tool.

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 'Use when you need to browse, search, look up, or discover what tools exist for...' and 'Call this FIRST when you have many tools available'. Provides clear context and implicit exclusions.

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

All Irail toolsInspect
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TDQS

A4/5.0
Disambiguation4/5

Most tools have distinct purposes with detailed descriptions. However, the three ask_pipeworx variants (standard, beta, grounded) and the five polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) have overlapping functionality that could confuse an agent if descriptions are not carefully parsed. The memory tools (remember, recall, forget) are clear. Overall, the set is mostly disambiguated.

Naming Consistency3/5

Tool names use snake_case consistently, but naming patterns vary: some follow verb_noun (e.g., ai_visibility_check, compare_entities), others are noun_noun (e.g., bet_research, entity_profile), and a few are single verbs (e.g., recall, remember, forget, subscribe). This mixed pattern reduces predictability, though the names are still readable.

Tool Count2/5

At 35 tools, the server is overly broad, covering Belgian rail, Pipeworx data queries, prediction markets, memory, subscriptions, and more. The server name 'Irail' suggests a focused rail toolset, but rail is only a small part. This scope mismatch and high tool count make it feel bloated and less coherent.

Completeness4/5

Within each subdomain, the tool surface is fairly complete. Belgian rail has journey planning, liveboard, train tracking, and disturbances. Data querying has universal, grounded, deep research, entity profiles, comparisons, recent changes, and claim validation. Prediction markets have arbitrage, edge detection, edge tracking, fill risk, and cross-venue spread. Memory and subscriptions are covered. Minor gaps exist (e.g., no tool for deleting entities or canceling orders), but overall coverage is strong.

Resources