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Glama

Linkedin_ads

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

A4.9/5.0
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 substantial behavioral context: fan-out to multiple sources, GDELT→GNews fallback, soft-fail for patents, output format with changes[] and pipeworx:// URIs. No contradiction with 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?

The description is a single coherent paragraph, front-loaded with query examples, then explains sources, parameters, and distinguishes from sibling. Every sentence serves a purpose; no wasted words.

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 specifies the return structure (changes[] grouped by source, total_changes, pipeworx:// URIs). It covers all three parameters with usage guidance, source behaviors, and fallbacks. For a tool with three required params and no nested objects, this is thorough and complete.

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 description coverage is 100%, so baseline is 3. The description adds useful context beyond schema: examples of relative shorthand for since (e.g., '30d'), typical monitoring window ('Use 30d or 1m'), and clarification that value accepts ticker or zero-padded CIK. This elevates it to a 4.

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 concrete query examples ('What's new with X', 'latest on Y'), then states the tool returns a change feed for a company in a window, listing specific sources (SEC, GDELT/GNews, USPTO). It explicitly distinguishes itself from sibling entity_profile, making purpose crystal clear.

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 provides explicit examples of when to use this tool ('What's new with X') and directly tells when to use the alternative entity_profile instead ('Use entity_profile instead when you want the static profile'). It also explains fallback behavior between GDELT and GNews.

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 have overlapping purposes (multiple ask_pipeworx variants, several polymarket tools, and similar research functions). The agent would struggle to pick the right tool among 36, especially with unclear boundaries between some tools.

Naming Consistency2/5

Tool names lack a consistent pattern. The li_ prefix for LinkedIn tools is isolated; other tools use random prefixes (pipeworx_, polymarket_) or no prefix at all (ask_pipeworx, bet_research). Mix of verb_noun and noun structures.

Tool Count2/5

36 tools for a server named 'Linkedin_ads' is excessive. Only 5 tools are actually LinkedIn ads-related; the rest are unrelated, making the server feel bloated and unfocused. A focused server would have 5-10 tools.

Completeness2/5

For the stated LinkedIn ads domain, the tool set is incomplete: missing create/update/delete campaigns, budget management, and targeting. While the server includes many other data tools, its advertised purpose is poorly served.

Resources