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Open Contracting

by io.github.pipeworx-io

AI Visibility Check

ai_visibility_check
Read-onlyIdempotent

Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass _apiKey to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entityYesThe thing to ask about. Brand/business name, product name, person, or topic. E.g. "Pipeworx", "OpenInvoice", "Acme Corp pricing".
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key (sk-ant-...) — only needed if "anthropic" is in models. Passed straight through to api.anthropic.com.
contextNoOptional: a phrase locating the entity (e.g. "Boston restaurant", "B2B SaaS"). Helps disambiguate common names.

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, establishing safety. The description adds behavioral context: default model, free tier, BYO key for Anthropic, and output structure, enriching transparency 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?

The description is extremely concise—two sentences—with no wasted words. It front-loads the primary action and effectively uses whitespace for readability.

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 partially explains return structure (per-model fields + combined view). Parameters are fully covered. Slightly incomplete on output details but adequate for a safe, simple 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 coverage is 100%, so baseline is 3. The description adds meaningful context: default model for 'models', purpose of '_apiKey', and disambiguation role of 'context', surpassing schema alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool probes LLMs about an entity and scores visibility, with specific verb and resource. It does not explicitly differentiate from siblings like 'scan_competitor_ai_presence', but the purpose is well-defined.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description mentions use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') but does not provide when-not-to-use or contrast with sibling tools, leaving usage context implied rather than explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Open Contracting toolsInspect
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TDQS

A3.7/5.0
Disambiguation2/5

Several tool clusters overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve query/discovery purposes; polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, and bet_research all target prediction-market opportunity detection. ask_pipeworx_beta is explicitly identical to ask_pipeworx right now, making the distinction essentially invisible without deep description parsing.

Naming Consistency3/5

Names mix domain prefixes (oc_*, polymarket_*, pipeworx_*), action verbs (validate_claim, resolve_entity, generate_llms_txt), and plain nouns (entity_profile, recent_alerts, recent_changes). Most are readable snake_case, but there is no uniform verb_noun or domain-first convention, so the set feels stylistically fragmented rather than patterned.

Tool Count2/5

35 tools is a heavy surface for a server ostensibly named 'Open Contracting' — only 4 of the 35 tools actually relate to open contracting data. The rest sprawl across general data lookup, prediction markets, memory, subscriptions, npm scanning, and AI visibility, making the tool count feel bloated and unfocused relative to the stated purpose.

Completeness2/5

For the open-contracting domain implied by the server name, the surface is incomplete: there is coverage metadata, search, recent releases, and process history, but no direct retrieval of a single release by ID and no broader OCDS exploration tools. As a general data/Pipeworx toolkit the coverage is wide, but the severe mismatch between the server name and the actual tool set creates a significant gap between expectation and capability.

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