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Glama

Serper

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

A4.6/5.0
Behavior5/5

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

Annotations provide readOnlyHint, idempotentHint, destructiveHint; the description adds that results include full schemas with curated examples and are ready to call directly, explaining behavior beyond annotations. No contradiction.

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?

Description is three sentences long, front-loading the core purpose. Each sentence contributes essential information (purpose, use cases, output details). Slightly verbose for a discover tool but still 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 no output schema, the description fully explains that returns include names, descriptions, and full input schemas. With 6 parameters all documented and examples provided, the description covers all necessary context for an AI 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 100%, but the description adds value by explaining aliases (task, q, description, search) and providing natural language examples. This helps the agent understand parameter flexibility 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 the tool discovers tools by describing data or task, with a specific verb ('Find tools') and resource ('tools'). It provides extensive examples of supported domains (SEC filings, FDA drugs, etc.) and distinguishes it from siblings like search or deep_research by focusing on tool discovery.

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

Usage Guidelines4/5

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).' It also lists use cases like browsing or discovering tools. While it doesn't explicitly say when not to use, the guidance is clear enough for an AI agent.

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

A3.9/5.0
Disambiguation3/5

The ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) has blurry boundaries — ask_pipeworx_beta is explicitly identical to ask_pipeworx today — and the six Polymarket tools (polymarket_arbitrage, polymarket_edges, polymarket_fill_risk, etc.) all operate in the opportunity-detection space. Extremely detailed descriptions help, but an agent could easily select the wrong variant.

Naming Consistency3/5

Snake_case is used throughout and the polymarket_* and pipeworx_* clusters are internally consistent, but the set mixes verb-first names (ask_pipeworx, resolve_entity, validate_claim) with noun-first names (entity_profile, recent_changes, news, places) roughly evenly. The 'beta' suffix on a stable production tool and the adjective-noun 'deep_research' add further inconsistency.

Tool Count2/5

At 34 tools this exceeds the 25+ threshold, and the count is padded with redundancy: three near-identical ask_pipeworx variants, ai_visibility_check wrapped by scan_competitor_ai_presence, and six overlapping Polymarket tools. The unusually broad multi-domain scope justifies more tools than a typical server, but several clusters could be consolidated.

Completeness4/5

The surface is thorough for a read-only data-access gateway: universal routing, grounded verification, entity resolution/profiles/comparisons, web/news/maps search, prediction-market analysis, memory CRUD, and a full subscription lifecycle. Minor gaps exist (no direct fetch tool for pipeworx:// citation URIs, no image/video Serper endpoints) but agents can work around them.

Resources