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Spain Tenders

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 and idempotentHint, so the bar for additional transparency is lower. The description adds valuable process details: 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format.' It also describes the output as a single text blob, which is useful. This goes beyond the annotations without contradicting them.

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 three sentences, front-loaded with the core purpose, followed by the process and practical use cases. Every sentence adds value, and there is no redundancy or fluff.

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?

Given the tool's simplicity, the description covers the output format, process, and typical use cases. It does not have an output schema, but the description explicitly states the output is a 'standard llms.txt markdown format' and a 'single text blob,' which is sufficient for an agent to understand the result.

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%, so the baseline is 3. The description does not add new meaning to the parameters (url and max_links) beyond what the schema already provides. It mentions 'any URL' as a generic qualifier but doesn't elaborate on parameter behavior or constraints.

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 starts with a specific verb-resource pair ('Generate a production-ready llms.txt file') and clearly differentiates this tool from siblings by stating the target output format and use cases. It distinguishes itself from related tools like ai_visibility_check or scan_competitor_ai_presence by focusing on the llms.txt artifact.

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 provides clear use cases ('getting a client's site indexed by AI', 'drafting llms.txt for your own project', 'auditing how an AI crawler would see a competitor'), which implies appropriate contexts. However, it does not explicitly mention when not to use this tool or compare it to potential alternatives, so it stops short of a 5.

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

A3.7/5.0
Disambiguation2/5

Several clusters overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical routers (the beta currently matches stable exactly), and the Polymarket tools all circle around edge/arbitrage detection with fuzzy boundaries. The Spain tender tools and utility tools are distinct, but the overlapping clusters are enough to cause misselection.

Naming Consistency3/5

Names are uniformly snake_case and some families share clear prefixes (es_tender_*, ask_pipeworx_*, polymarket_*). However, conventions are mixed: verb-led names like compare_entities and subscribe sit alongside noun phrases like entity_profile and bet_research, so there is no consistent verb_noun pattern.

Tool Count2/5

34 tools is already in the heavy range, but the bigger problem is scope: a server named 'Spain Tenders' ships 34 tools, only 3 of which are actually Spanish-procurement tools. The rest are a broad Pipeworx/Polymarket/utility toolkit, making the count inappropriate for the declared purpose.

Completeness3/5

The three es_tender_* tools cover the core discovery workflows: keyword search, recent notices, and status filtering with budgets, deadlines, and URLs. Notable gaps remain, though: no tender detail-by-id tool, no tender-specific subscription/alerting, and no explicit region, CPV, or date-range filters.

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