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Coresignal

by io.github.pipeworx-io

Generate llms.txt

generate_llms_txt
Read-onlyIdempotent

Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesFull URL of the site to summarize, e.g. "https://example.com" or a specific landing page.
max_linksNoMaximum number of link entries to include (default 25, max 50).

TDQS

A4.5/5.0
Behavior4/5

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds the behavioral context that it fetches the page, extracts title/description/key links, and emits standard markdown. No contradictions.

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 a single paragraph with the core purpose in the first sentence, followed by additional details. It is efficient, front-loaded, and every sentence adds value without redundancy.

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 2 parameters, no output schema, and comprehensive annotations, the description covers the process (fetch, extract, emit), output format (single text blob), and use cases. It is complete for the tool's simplicity.

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% with descriptions for both parameters. The description adds value by explaining url as 'Full URL of the site to summarize' and max_links with default 25 and max 50, going beyond the schema.

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 generates an llms.txt file for AI crawlers, specifying the action (generate), resource (llms.txt for a URL), and purpose (indexing). It distinguishes itself from the varied sibling tools, none of which have the same function.

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 lists explicit use cases: getting a client's site indexed, drafting for own project, or auditing competitors. While it does not mention when not to use it, the sibling list has no direct alternative, and the use cases are clear.

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.

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