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Coresignal

by io.github.pipeworx-io

Coresignal Employee

coresignal_employee
Read-onlyIdempotent

Find a person's professional profile by name (+company) via Coresignal — returns title, current company, location, LinkedIn URL, work experience and education. LinkedIn-adjacent people data. Example: coresignal_employee({ name: "Patrick Collison", company: "Stripe", _apiKey: "your-key" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesFull name of the person, e.g. "Patrick Collison".
_apiKeyYesCoresignal API key (BYO). Sign up + start a trial at coresignal.com.
companyNoOptional company name to disambiguate, e.g. "Stripe".

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, non-destructive behavior. Description adds specifics about returned data (title, company, location, LinkedIn URL, experience, education) and notes it's 'LinkedIn-adjacent', providing useful context.

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 an example, front-loaded with purpose. No unnecessary words.

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?

While no output schema exists, the description lists returned fields and provides an example. It covers the main aspects for a simple lookup, though pagination and error handling are omitted.

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 covers all parameters (100% coverage). The description adds an example call showing usage, but does not add extra semantic meaning beyond what the schema provides.

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 finds a person's professional profile by name and optionally company, listing returned fields. However, it does not differentiate from siblings like entity_profile or resolve_entity.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives such as coresignal_company or other people discovery tools. The description only states what it does, not the best context for use.

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

All Coresignal toolsInspect
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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