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Glama

Data Baltimore

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

A4.8/5.0
Behavior5/5

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

Annotations declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, aligning with a discovery tool. The description adds that it 'returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly,' fully disclosing behavior and output.

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 moderately concise, front-loading the main purpose. It packs useful information but could be slightly trimmed. Overall efficient and well-structured.

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 and 6 parameters, the description is complete. It explains return values (top-N tools with names, descriptions, schemas), usage context, and parameter aliases. No gaps for a discovery tool.

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%, so baseline is 3. The description adds value by explaining that 'query' accepts aliases (task, q, description, search) and provides natural language examples, enhancing understanding 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 'Find tools by describing the data or task.' It uses a specific verb (find/discover) and resource (tools). It also distinguishes itself from sibling tools by focusing on tool discovery rather than direct queries.

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 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' Provides concrete examples of when (SEC filings, stocks) and implies when not to use (when tool is already known).

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

A4.1/5.0
Disambiguation2/5

Several tool groups overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to the same catalog; entity_profile, recent_changes, and compare_entities cover overlapping company-data territory; ai_visibility_check and scan_competitor_ai_presence are near-duplicates. Agents would frequently need to read long descriptions to pick the right tool.

Naming Consistency4/5

All tool names use snake_case and mostly follow verb_noun patterns (ask_pipeworx, compare_entities, resolve_entity, validate_claim). Minor inconsistencies exist: generic noun-only names like entity_profile, recent_changes, and bet_research, plus inconsistent prefixes (ask_, baltimore_, polymarket_, pipeworx_, scan_) that group by domain rather than action.

Tool Count3/5

34 tools is on the heavy side for a data-access server, though the scope is broad. Several tools feel tangential to the core data mission (remember/recall/forget, generate_llms_txt, pipeworx_feedback, pipeworx_trending), and the ask_pipeworx family plus the six polymarket_* tools inflate the count with overlapping functionality.

Completeness4/5

The surface covers the domain well: discovery (discover_tools, suggest_questions, baltimore_layers), lookup (ask_pipeworx, baltimore_query/recent), grounded verification (ask_pipeworx_grounded, validate_claim), comparison (compare_entities), profiling (entity_profile), change tracking (recent_changes), and prediction-market analysis. Minor gaps include no direct web search and no Baltimore-specific export/bulk operations, but agents can work around these.

Resources