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Grants Gov

by io.github.pipeworx-io

Scan Competitor AI Presence

scan_competitor_ai_presence
Read-onlyIdempotent

Compare AI visibility across multiple entities side-by-side. Probes each entity (your brand + N competitors) with ai_visibility_check, ranks by score, surfaces which is most/least recognized. Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?". Returns ranked list with score, confidence, signal density per entity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key — only if "anthropic" is in models. Passed to api.anthropic.com per probe.
contextNoOptional shared context applied to every probe (e.g. "B2B SaaS", "Boston restaurant"). Disambiguates common names.
entitiesYesArray of 2-8 entities to compare (brand/business/product names). First entry treated as the "subject" for narrative; rest are competitors.

Schema Changelog

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

  1. Added

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already indicate read-only, open-world, idempotent, and non-destructive behavior, so the description's main contribution is revealing the probing process (calling ai_visibility_check for each entity) and the output structure (ranked list with score, confidence, signal density). 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.

Conciseness5/5

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

The description is concise, with no wasted words. It front-loads the main action ('Compare AI visibility across multiple entities side-by-side') and then efficiently explains the process, use case, and output.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 100% schema coverage, annotations, and a clear description of the output (ranked list with fields), the description is nearly complete for an AI agent. The only minor gap is the lack of output schema, but the text sufficiently describes the return format.

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% with all parameters documented. The description adds value by clarifying that the first entity in 'entities' is treated as 'subject' for narrative purposes, which is not in the schema, and by explaining the role of 'context' in disambiguation.

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 explicitly states the tool compares AI visibility across multiple entities side-by-side, distinguishes from the sibling 'ai_visibility_check' by framing it as a comparative ranking tool, and uses specific verbs like 'probes', 'ranks', and 'surfaces'.

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?

The description provides a clear use case ('competitive AI-marketing audits') and an example question ('does Claude know about us as well as our competitors?'), implying when to use this over a single-entity check. It does not explicitly state when not to use it, but the context is sufficient.

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.

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