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Glama

Tinder Bio

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?

Beyond annotations (readOnly, openWorld, idempotent, non-destructive), the description reveals parallel fan-out across sources, GDELT preferred over GNews, USPTO deprecation, and return structure (changes grouped by source, total_changes count, citation URIs). No contradictions 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is comprehensive but not verbose; each sentence adds unique information. Front-loaded with natural language examples before technical details. Could be slightly tighter but overall well-organized.

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 lacking an output schema, the description fully documents return fields (changes[], total_changes, citation URIs). It also covers edge cases (soft-fail, rate limiting fallback) and parameter format details, making it self-sufficient for a complex multi-source 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 description coverage is 100%, so baseline is 3. Description adds value by providing usage examples for `since` ("30d" for typical monitoring) and explaining that `value` accepts ticker or CIK. This enriches the schema descriptions without being redundant.

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?

Description explicitly states the tool provides a change feed for a company over a time window by fanning out to multiple sources (SEC EDGAR, GDELT/GNews, USPTO). It gives concrete natural language examples of queries it answers ("What's new with X", "latest on Y") and distinguishes itself from sibling entity_profile via an explicit recommendation statement.

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?

Description includes when to use this tool versus entity_profile: 'Use entity_profile instead when you want the static profile...' It also explains fallback behavior (GDELT→GNews on rate limit/5xx) and USPTO soft-fail status, providing clear context for agent decision-making.

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
Disambiguation4/5

Most tools have clearly distinct purposes, especially within separate domains. However, the large number of Polymarket-related tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.) may cause confusion as they overlap in functionality, though descriptions help differentiate them.

Naming Consistency3/5

All tool names use snake_case, but the naming patterns are inconsistent: some are bare verbs (forget, recall), some are verb_noun (generate_llms_txt), and others are noun_compound (polymarket_arbitrage). This mixed convention reduces predictability.

Tool Count3/5

With 31 tools, the set is on the higher end. While many tools serve distinct data-querying and prediction-market needs, the count feels heavy for the server's stated purpose (Tinder Bio), and some tools (e.g., pipeworx_trending, pipeworx_feedback) could be considered bloat.

Completeness1/5

The server name implies a focus on dating profile bio generation, yet only one tool (tinder_bio_generate) addresses this. The vast majority of tools are unrelated (e.g., SEC filings, Polymarket bets), creating a severe mismatch between the server's title and its actual functionality.

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