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Glama

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

A4.5/5.0
Behavior4/5

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

Annotations already mark it read-only and idempotent. The description adds valuable behavioral context: returns top-N tools with full schemas and curated examples, and that results are 'ready to call directly, no second schema lookup needed.' This goes beyond the annotation safety profile and explains the response nature.

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: purpose, when-to-use with domain list, and return behavior. Each sentence earns its place, is front-loaded, and has no filler or redundancy. Extremely efficient for a meta-tool description.

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?

Even without an output schema, the description fully explains the return value (names, descriptions, full input schemas) and the intended use case. For a tool-discovery tool, this is complete: it covers how it behaves, what it returns, and when to invoke it. No obvious gaps for the agent to select and call it 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?

Input schema coverage is 100% with every parameter (including aliases) described. The description does not add new parameter-level details; it only mentions 'top-N' which corresponds to the existing 'limit' parameter. Baseline 3 is appropriate since schema handles the semantics.

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 starts with a specific verb and resource: 'Find tools by describing the data or task.' It clearly distinguishes itself from sibling tools like search_* or ask_pipeworx by focusing on discovering tools rather than operating on data. The list of domains further clarifies the intended scope.

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?

Explicit when-to-use guidance is provided: 'Use when you need to browse, search, look up, or discover what tools exist' and 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This also implies when not to use it (when you already know the specific tool and want a single answer).

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

A3.6/5.0
Disambiguation2/5

Many tools overlap in purpose: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all provide data retrieval with subtle differences. The Polymarket suite (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) and AI visibility tools (ai_visibility_check, scan_competitor_ai_presence) also have fuzzy boundaries. While some tools are clearly distinct, the overall set has significant ambiguity that could lead to misselection.

Naming Consistency2/5

Tool names mix conventions: some are verb_noun (search_networks, compare_entities, remember, forget), others are noun_compound (entity_profile, polymarket_edges, ask_pipeworx), and a few are verb-only (recall, forget). The pattern is inconsistent, with no clear naming strategy across the tool surface.

Tool Count2/5

With 34 tools, the server is heavily overloaded. While it serves as a general data platform, many tools are meta-level (discover_tools, suggest_questions) or peripheral (subscriptions, memory). The count feels excessive for the core purpose, and many tools could be consolidated or removed.

Completeness1/5

Despite the server name 'Peeringdb', only three tools (search_networks, search_facilities, search_exchanges) directly serve that domain. The vast majority of tools cover unrelated areas (Pipeworx data, Polymarket betting, AI visibility, memory, subscriptions). For the declared purpose of PeeringDB, the surface is severely incomplete—missing common operations like retrieving network details, viewing IX members, or managing peering policies.