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Glama

Macro Snapshot

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

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. Description adds that returns top-N tools with full schemas and examples, ready to call. No contradiction, and context is useful beyond annotations.

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?

Description is 4-5 sentences, front-loaded with purpose, then context, output details, and usage guideline. Every sentence adds value with no redundancy.

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?

Despite no output schema, the description fully explains what the output contains. All six parameters are covered. The description is complete for an agent to understand and invoke the tool 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?

All 6 parameters are fully described in the input schema (100% coverage). Description reinforces that query accepts natural language, but does not add new semantic meaning 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 it finds tools by describing data or task, explicitly listing many domains. It distinguishes itself from sibling tools like ask_pipeworx or deep_research by being a discovery tool, not for direct answers.

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?

The description explicitly says 'Call this FIRST when you have many tools available' and 'Use when you need to browse...'. It implies not to use when you already know the tool, but does not explicitly list alternatives.

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
Disambiguation2/5

Several tools form near-overlapping clusters: ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded/deep_research all route questions, and bet_research/polymarket_edges/polymarket_arbitrage/polymarket_fill_risk/polymarket_kalshi_spread all target prediction-market edges. ask_pipeworx_beta is explicitly identical to ask_pipeworx today, so an agent must read long descriptions to pick correctly. Most other tools are distinct, but the ambiguous clusters pull the score down.

Naming Consistency3/5

All names use snake_case and are readable, but conventions mix: many are verb_noun (ask_pipeworx, compare_entities, discover_tools, subscribe), several are noun phrases (macro_snapshot, indicator, entity_profile, polymarket_arbitrage), and a few are adjective_noun (recent_alerts, deep_research). The near-duplicate ask_pipeworx / ask_pipeworx_beta / ask_pipeworx_grounded suffixes form the only consistent family, but overall the naming pattern is not uniform.

Tool Count2/5

33 tools is well beyond the 15-tool well-scoped range and even past the 25-tool 'too many' threshold. The server tries to be a data router, prediction-market desk, AI visibility checker, memory store, and subscription manager all at once, and includes an intentional duplicate (ask_pipeworx_beta). Several tools (remember/recall/forget, subscribe/unsubscribe/list_subscriptions/recent_alerts) could be their own server.

Completeness4/5

The surface covers question answering, deep research, entity resolution/profile/comparison, macro indicators, prediction-market analytics, subscriptions, memory, and feedback—no obvious dead ends for the main workflows. Minor gaps exist (e.g., no direct generic web search, no update for saved memory other than overwrite, and some niche additions like generate_llms_txt feel out of place), but the core data and research lifecycle is well covered.

Resources