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Osrm

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.

TDQS

A4.7/5.0
Behavior5/5

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

The description adds significant behavioral context beyond annotations: it reveals the tool probes each entity with ai_visibility_check, ranks by score, and returns a ranked list with score, confidence, and signal density. This fully discloses the mechanism and output. No contradiction with annotations (readOnlyHint, etc.).

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 three sentences: purpose, mechanism, use case. Front-loaded with key information. Every sentence serves a purpose with no filler.

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?

Given no output schema, the description explains the return format (ranked list with score, confidence, signal density) and notes the underlying probe call. It covers the main behavioral aspects, though it could optionally mention any error conditions or rate limits. Overall sufficient for the tool's complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema coverage, the description still adds value by clarifying that the first entity in the array is treated as the 'subject' for narrative purposes and explaining model options with usage notes (e.g., 'workers-ai' as default, 'anthropic' requires key). It adds meaning beyond the schema descriptions.

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 clearly states the tool compares AI visibility across multiple entities side-by-side, using ai_visibility_check per entity and ranking results. It distinguishes from siblings like ai_visibility_check (single entity) and compare_entities (more generic).

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 directly states it is 'useful for competitive AI-marketing audits' and gives an example. While it does not explicitly exclude alternatives or provide when-not-to-use guidance, the context of comparing multiple entities implies it is the right choice for side-by-side analysis.

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
Disambiguation3/5

Many tools have distinct purposes, but there is notable overlap between ask_pipeworx, ask_pipeworx_grounded, and deep_research, all serving data retrieval. Similarly, the Polymarket betting tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.) share a domain, causing potential confusion for an agent.

Naming Consistency2/5

Tool names follow no consistent pattern: snake_case (ai_visibility_check), camelCase-like (bet_research, compare_entities), and noun-first (entity_profile, recent_changes) are mixed. The lack of a uniform verb_noun or other convention makes it harder to predict tool names.

Tool Count2/5

35 tools is excessive for a server named 'Osrm,' which suggests a focused routing engine. The actual tool set spans routing, data query, betting, entity resolution, and memory, indicating an overbroad scope that dilutes coherence.

Completeness3/5

The data query and betting tools are relatively comprehensive, but the routing side is minimal (missing isochrones, alternative routes). Gaps exist in general web search and coverage of other prediction markets. The server doesn't fully cover either the implied routing domain or the broader data/betting domain.

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