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Cta

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).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

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 value by explaining the process (fetches page, extracts title/description/key links, emits markdown) and the output format. 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 concise (3 sentences), front-loaded with the main purpose, followed by process details and use cases. 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?

For a tool with 2 simple parameters and no output schema, the description fully covers input, output, and use cases. It explains the output format and purpose, making it complete for an AI agent.

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%, with both 'url' and 'max_links' described in the schema. The description reinforces the 'max_links' default and max values but does not add new meaning beyond the schema. 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 uses a specific verb-resource combination ('Generate a production-ready llms.txt file') and lists concrete use cases (client's site indexing, drafting for own project, auditing competitor). It clearly distinguishes from sibling tools, which are all unrelated.

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 explicitly states when the tool is useful ('Useful for: ...') and provides context for AI crawlers. It does not explicitly list when not to use or mention alternatives, but the context is clear and sufficient for an AI agent to decide.

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

A3.6/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all accept natural-language factual questions, and ask_pipeworx_beta is explicitly described as currently identical to ask_pipeworx. The entity tools (entity_profile, compare_entities, recent_changes, resolve_entity) and the many Polymarket tools also blur together, making misselection likely.

Naming Consistency3/5

Most names are snake_case and readable, with recognizable prefixes like cta_, polymarket_, and ask_pipeworx_. However, conventions are mixed: bare verbs (remember, forget, subscribe), noun-style phrases (entity_profile, bet_research), and variants like ai_visibility_check vs scan_competitor_ai_presence prevent a single predictable pattern.

Tool Count2/5

35 tools is above the comfortable range, and the count is especially mismatched for a server named 'Cta': only 4 tools actually concern Chicago transit, while the other 31 form a general-purpose research, prediction-market, and memory suite. The set feels like multiple unrelated servers merged together.

Completeness2/5

As a CTA server, the surface is notably incomplete: it has bus/train positions and predictions but lacks alerts, service disruptions, route listings, and station/stop metadata. The broader Pipeworx tools are extensive but appear bolted on, so the overall set has no coherent domain against which completeness can be judged.

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