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Open Contracting

by io.github.pipeworx-io

Ask Pipeworx — Grounded

ask_pipeworx_grounded
Read-onlyIdempotent

Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 5,738 across 1499 sources, fills arguments, fetches the data — then EXTRACTS the answer using ONLY what the tool result contains. Returns {answer, evidence (verbatim quote), confidence, source, fetched_at, refusal_reason:null} on success, OR an explicit refusal {answer:null, refusal_reason:"not_in_source"|"no_tool_match"|"tool_error"|"data_truncated"|"llm_error"} when the data doesn't directly answer. Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements). Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoAlias for question.
textNoAlias for question.
inputNoAlias for question.
queryNoAlias for question.
promptNoAlias for question.
questionYesYour question in natural language. Accepts query, q, prompt, text, input as aliases.

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses the refusal behavior with specific refusal_reason values, the exact success return shape, the extra LLM call cost, and the grounded extraction constraint. This gives the agent a clear model of what happens when data does or does not support an answer.

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 dense but every sentence serves a purpose: core behavior, routing context, grounding constraint, return contract, refusal contract, use cases, and cost trade-off. It is front-loaded with the highest-stakes distinguishing information before the structural details.

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?

With no output schema present, the description fully documents the return value and refusal modes, making the tool's behavior predictable. It also covers routing scale, argument filling, data fetching, and when to prefer the sibling, so an agent has everything needed to invoke it correctly.

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

Parameters3/5

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

Schema description coverage is 100%, with every parameter documented as an alias for the natural-language question. The tool description adds no parameter-specific meaning, but none is needed since the schema already fully explains the single conceptual input.

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 identifies a specific behavior: hallucination-resistant grounded answering that routes through the same tool-selection pipeline as ask_pipeworx but extracts answers only from tool results. It clearly distinguishes this tool from its sibling ask_pipeworx on the basis of factual rigor and refusal behavior.

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?

It explicitly states when to use this tool: whenever the answer will be quoted, cited, or acted on, and the agent must not invent facts. It also names the alternative, ask_pipeworx, and gives a concrete exclusion condition: prefer ask_pipeworx for casual lookups because this mode costs one extra LLM call.

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.

Resources