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Coresignal

by io.github.pipeworx-io

Coresignal Company

coresignal_company
Read-onlyIdempotent

Look up a company's firmographics + headcount (size, employee count, industry, founding year, HQ, LinkedIn URL) from Coresignal by company name or website. LinkedIn-adjacent B2B data. Example: coresignal_company({ name: "Stripe", _apiKey: "your-key" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoCompany name to look up, e.g. "Stripe", "OpenAI". Provide this OR `website`.
_apiKeyYesCoresignal API key (BYO). Sign up + start a trial at coresignal.com.
websiteNoCompany website/domain, e.g. "stripe.com". Provide this OR `name`.

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds context about the data source (LinkedIn-adjacent B2B data) and the fields returned (size, employee count, industry, etc.), which is useful beyond annotations. It does not contradict 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?

Two sentences plus a concise example. Front-loaded with purpose. Every sentence adds value (purpose, parameters, example data). No extraneous content.

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 lacking an output schema, the description lists the key return fields (firmographics, headcount, industry, founding year, HQ, LinkedIn URL), which is sufficient for a simple lookup tool. Annotations provide safety guarantees. No gaps.

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 coverage is 100% (all three parameters described in schema). The description restates the name and website parameters and provides an example, but adds no new semantic information beyond what the schema already conveys. Baseline 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 'Look up a company's firmographics + headcount' from Coresignal by name or website, specifying the action and resource. It distinguishes itself from the sibling tool 'coresignal_employee' which focuses on employee lookup.

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 indicates when to use the tool (by company name or website) and provides an example call. However, it does not explicitly state when not to use it or compare to alternatives like 'coresignal_employee' or other lookup tools, leaving some inference to the agent.

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