Skip to main content
Glama

Fara

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

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. The description adds that results are 'ready to call directly, no second schema lookup needed' and mentions top-N relevance. It doesn't contradict annotations.

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 description is a single paragraph that front-loads purpose and domain list, then explains return value and usage. It is effective but could be slightly more concise by separating usage guidance.

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?

With no output schema, the description fully explains what is returned (names, descriptions, schemas with examples). It also provides context for when to use it. Complete for a discovery tool.

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%, so the description adds limited value beyond the schema. It includes example queries and notes aliases, but these are already in the schema. Baseline of 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?

The description clearly states 'Find tools by describing the data or task' and enumerates specific domains (SEC filings, FDA drugs, etc.). It distinguishes itself from sibling tools by being the discovery mechanism, not a specific data 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 advises 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This gives clear when-to-use guidance and implies it precedes tool invocation.

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

All Fara toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.8/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx_beta is explicitly identical to ask_pipeworx, and the multiple Polymarket tools (polymarket_edges, polymarket_arbitrage, bet_research, polymarket_edge_tracker, polymarket_fill_risk) all surface betting opportunities with only subtle differences. The line between ask_pipeworx, ask_pipeworx_grounded, deep_research, and discover_tools is also fuzzy, making misselection likely.

Naming Consistency3/5

All tool names use snake_case and are readable, but naming conventions are mixed: many follow verb_noun (ask_pipeworx, discover_tools, list_foreign_principals), while others use noun-first or adjective_noun (entity_profile, polymarket_arbitrage, recent_alerts). Several tools share the 'pipeworx_' or 'polymarket_' prefix without that prefix meaning a consistent action type.

Tool Count2/5

34 tools is high for a single MCP server, and the scope spans unrelated domains (general data lookup, prediction-market analytics, FARA registrations, memory, subscriptions, AI-visibility monitoring). This feels like several servers merged rather than one cohesive set; many tools could be split into focused modules without losing functionality.

Completeness4/5

Core workflows are well covered: flexible data querying (ask_pipeworx, grounded, deep_research), entity resolution, FARA search/document retrieval, memory CRUD, subscription lifecycle, and claim validation. There are minor gaps such as no direct tool for single-source parameterized queries (everything routes through the universal router) and no account/profile management, but agents can complete the main advertised tasks.

Resources