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Glama

Imf

by io.github.pipeworx-io

Recent Changes

recent_changes
Read-onlyIdempotent

"What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since since), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). since accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type. Only "company" supported today.
sinceYesWindow start — ISO date ("2026-04-01") or relative ("7d", "30d", "3m", "1y"). Use "30d" or "1m" for typical monitoring.
valueYesTicker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193").

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds valuable behavioral detail: fan-out to multiple sources, GDELT→GNews fallback on rate limits/5xx, USPTO soft-fail due to API sunset, and the return structure (changes[], total_changes, citation URIs). This is rich context beyond what annotations provide.

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?

The description is dense but well-organized: it opens with query examples to convey intent, then explains the fan-out sources, parameter semantics, output shape, and alternatives. Every sentence adds value, and the length is justified by the tool's multi-source complexity. No filler or 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?

Given the tool's complexity (three data sources, fallback logic, parameter flexibility) and no output schema, the description is remarkably complete. It explains what the tool returns, how the window parameter works, what sources are consulted, fallback behavior, edge cases (USPTO sunset), and how this tool differs from a likely sibling (entity_profile). Nothing critical is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers 100% of parameters with descriptions, but the tool description adds significant extra semantics: it explains 'since' accepts ISO dates or relative shorthand including examples ('7d', '30d', '3m', '1y'), recommends '30d'/'1m' for typical monitoring, and clarifies that 'value' can be a ticker or zero-padded CIK. These details enhance usability 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's purpose: a change feed for a company over a time window, covering filings, news, and patents. It distinguishes itself from siblings by explicitly contrasting with entity_profile (static profile) and providing example queries that signal its intended use case.

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?

Provides explicit when-to-use guidance via natural language examples like "What's new with X" and "latest on Y". It also names the alternative tool (entity_profile) and explains when to prefer it, which goes beyond mere implication and gives clear decision criteria.

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 natural-language query tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim) have heavily overlapping purposes, and ask_pipeworx_beta currently behaves identically to ask_pipeworx. The five Polymarket tools also have subtle boundaries, though the IMF, memory, and subscription clusters are clearly separated.

Naming Consistency4/5

Most tools follow a clean snake_case verb_noun pattern (get_data, resolve_entity, subscribe, compare_entities). Minor deviations exist: noun-first names like entity_profile and ai_visibility_check, brand-prefixed names like pipeworx_feedback and pipeworx_trending, and ask_pipeworx_beta using a suffix instead of an underscore.

Tool Count2/5

34 tools is well above the range that remains easily navigable, and the count is inflated by many meta-tools, overlapping query entry points, and five distinct Polymarket tools. The server is named Imf, yet it also carries npm dependency scanning, llms.txt generation, AI visibility checks, and prediction-market tooling, making the scope feel unfocused.

Completeness3/5

Subdomain lifecycles are reasonably covered: memory has remember/recall/forget, subscriptions have subscribe/list/unsubscribe/recent_alerts, and data access has discovery, lookup, grounding, and research paths. However, the overall domain is so broad that a complete surface is hard to define, and the IMF-specific portion is thin (only get_data, get_datasets, and search_indicators).

Resources