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Coresignal

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

A4.6/5.0
Behavior5/5

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

Annotations already indicate readOnly, openWorld, and idempotent behavior, and the description adds significant context beyond that: exact success and refusal return shapes, refusal reasons, the extra LLM call cost, and the guarantee not to invent facts. There is no contradiction 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.

Conciseness4/5

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

The description is dense but each sentence adds valuable information: purpose, mechanism, output contract, refusal behavior, use cases, and cost tradeoff. It is longer than minimal, but not bloated; the slight length is justified by the richness of behavior it needs to convey.

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?

Despite having no output schema, the description fully specifies return values and refusal reasons. It also covers when to use the tool, how it differs from the sibling, and cost implications, making it sufficient for an agent to select and invoke the tool 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 all six parameters documented as aliases for the same natural-language question. The description adds no parameter-level meaning beyond what the schema already provides, so the baseline score of 3 is appropriate.

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 is a hallucination-resistant answer mode for high-stakes reads, and explicitly contrasts it with ask_pipeworx by emphasizing extraction only from tool results. This makes its purpose and differentiation from siblings immediately clear.

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?

The description gives explicit usage guidance: use this mode when answers will be quoted, cited, or acted on, and prefer ask_pipeworx for casual lookups. It names the alternative and the cost tradeoff, leaving no ambiguity about when to choose this tool.

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

A3.8/5.0
Disambiguation3/5

Several tools form tight families with overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all handle research queries, and entity_profile/compare_entities/recent_changes aggregate overlapping data. The descriptions are thorough and do distinguish them, but an agent could easily select the wrong member of a family for a given query.

Naming Consistency3/5

Most tools use snake_case, but conventions vary: verb_noun (list_subscriptions, resolve_entity), domain-prefixed nouns (polymarket_arbitrage, coresignal_company), bare verbs (remember, forget, recall), and an ask_* family (ask_pipeworx, ask_pipeworx_grounded). Patterns are predictable within clusters but there is no uniform server-wide convention.

Tool Count2/5

33 tools is well beyond the comfortable range, and the server named 'Coresignal' carries only two Coresignal-branded tools while also hosting prediction-market analysis, memory utilities, npm dependency scanning, llms.txt generation, and feedback mechanisms. The breadth feels bloated even though the core research platform is substantial.

Completeness4/5

The research/QA domain is well covered: simple lookup, grounded verification, deep multi-source research, entity resolution and profiling, comparisons, claim validation, subscriptions, and memory. Meta-tools like discover_tools and suggest_questions help navigation. Minor gaps exist (e.g., no standalone bulk-download or export tool), but there are no obvious dead ends.

Resources