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Grants Gov

by io.github.pipeworx-io

Pipeworx Trending

pipeworx_trending
Read-onlyIdempotent

What other AI agents are calling on Pipeworx right now. Returns the top tools, top packs, and total call volume over a recent window (24h, 7d, or 30d). Useful for: (1) discovering what data sources are hot for current events, (2) confirming a popular tool is the canonical choice before asking your own question, (3) seeing whether your use case aligns with what most agents need. Self-aggregating signal — derived from CF analytics-engine, no PII, just (pack, tool, count). Cached 5min-1h depending on window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
windowNo24h (default) | 7d | 30d. Shorter windows surface what's hot right now; longer windows show steady-state demand.

Schema Changelog

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

  1. Added

TDQS

A4.7/5.0
Behavior5/5

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

Discloses caching behavior (5min-1h), data source (CF analytics-engine), privacy (no PII), and data format (pack, tool, count). Annotations already indicate read-only, idempotent, non-destructive, but description adds significant behavioral details 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 concise (three sentences plus a bulleted list) with no wasted words. Every sentence adds value, and the structure is easy to parse.

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 simple tool (one optional parameter, no output schema, comprehensive annotations), the description covers purpose, usage, behavioral details, and parameter semantics completely. It is sufficient for an agent to invoke correctly.

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 covers 100% with enum and description. Description adds guidance on window selection: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' This enriches understanding 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?

Description clearly states the tool returns top tools, packs, and call volume over a window. It provides specific use cases (discovering hot data sources, confirming canonical tools, aligning use cases) and distinguishes itself from siblings like discover_tools by focusing on trending signals.

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?

Explicitly lists three useful scenarios (discovering hot data sources, confirming popular tools, aligning use cases). Does not mention when not to use or alternative sibling tools, but the context is clear enough for an AI agent to decide.

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: ask_pipeworx/ask_pipeworx_grounded/deep_research all handle broad queries; multiple Polymarket tools exist for edge finding; entity_profile/compare_entities/recent_changes/resolve_entity overlap on company data. An agent would struggle to pick the right tool.

Naming Consistency2/5

Naming patterns are mixed: some use verb_noun (search_opportunities, get_opportunity), some are phrases (ask_pipeworx_grounded, polymarket_edge_tracker), and some are vague (recall, forget). No consistent convention across the set.

Tool Count2/5

32 tools is high, but the core issue is that the server name 'Grants Gov' implies a narrow focus, yet only 2 tools are about grants. The sheer number of unrelated tools makes the set feel bloated and unfocused.

Completeness3/5

For the actual domain of general data querying and prediction markets, the tool surface is fairly complete, covering many sources. However, for 'Grants Gov' it is severely incomplete (missing all but opportunities). Overall, the scope is broad but lacks depth in any one area.

Resources