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Glama

Intercom

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. Added

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds substantial context: parallel call, fan-out to multiple sources, fallback behavior (GDELT→GNews), soft-fail for USPTO, and accepted date formats. No contradictions.

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 well-structured, starting with example queries, then explaining functionality and fallback, then parameter details, then comparison with sibling. Every sentence adds value, though slightly verbose for some readers.

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 thoroughly explains return structure (changes[] grouped by source, total_changes count, pipeworx:// URIs), covers all sources and fallback, addresses parameter formats, and contrasts with entity_profile. Complete for the tool's complexity.

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% with descriptions for all three parameters. The description adds extra meaning: typical `since` values ('30d' or '1m'), explanation that `type` only supports 'company', and clarifies `value` accepts ticker or CIK. This goes beyond 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 provides specific verb phrases ("What's new with X", "latest on Y") and clearly states the tool aggregates change feeds from SEC EDGAR, GDELT/GNews, and USPTO. It explicitly distinguishes from sibling tool entity_profile.

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?

The description includes explicit when-to-use guidance with real query examples and contrasts with entity_profile: 'Use entity_profile instead when you want the static profile...'. No ambiguity about 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

B3.4/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route factual questions to the same underlying catalog with unclear boundaries. Polymarket tools also overlap (bet_research, polymarket_edges, polymarket_arbitrage), and ai_visibility_check vs scan_competitor_ai_presence are nearly the same operation.

Naming Consistency3/5

Most tools follow a readable snake_case verb_noun pattern (ask_pipeworx, entity_profile, resolve_entity, list_subscriptions). However, the set mixes two distinct naming families — ic_* for Intercom tools and pipeworx/* for the rest — and includes ambiguous generic names like remember/recall/forget that don't visually connect to the rest.

Tool Count2/5

36 tools is far more than needed for an Intercom-focused server; the vast majority have nothing to do with Intercom and instead cover a sprawling Pipeworx data-research, prediction-market, and memory/subscription toolkit. The count alone is in the 'too many' range, and the scope mismatch makes it feel even more inflated.

Completeness2/5

For a server named Intercom, the surface is severely incomplete: only read/list/search operations exist for contacts and conversations, with no create, update, delete, send-message, or company-detail operations. The unrelated Pipeworx tools are fairly broad, but they don't compensate for the missing Intercom lifecycle coverage that the server name promises.

Resources