Rank a list of documents by MEANING closeness to a query (cosine similarity over embeddings). Ready-to-use RAG, no need for the agent to run its own vector DB. input='query || doc 1 || doc 2 || ...' (|| separated). [x402: 0.003 USDC on Base, pay-per-use]
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | 'consulta || doc1 || doc2 || ...' |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden, and it delivers relevant details: cosine-similarity semantics, delimiter-based input format, and the pay-per-use cost on Base. It does not disclose the output format (e.g., scores vs. order only) or limitations like max documents, but the core behavior is well-described.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded with the primary purpose, followed by input format and pricing. Every sentence earns its place: no filler, no repetition of schema details, and the parenthetical cost note is appropriately short.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with no output schema, the description covers the needed invocation details: the input encoding, ranking behavior, and usage-value proposition. The only notable absence is an explicit statement that the result is an ordered list (and whether scores are attached), but 'rank' already implies the output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the description largely restates the schema's 'consulta || doc1 || doc2 || ...' string. The additional explanatory format ('query || doc 1 || doc 2') is useful but not materially richer than the schema, placing this at the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names the exact operation ('Rank a list of documents'), the resource ('documents'), and the meaning-based criterion ('cosine similarity over embeddings'). It also differentiates itself from likely siblings like 'embeddings' by framing the tool as ready-to-use RAG, making the tool's role unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states when to use it by pointing to ready-to-use RAG without needing a vector DB, and gives an explicit input format. It does not describe explicit when-not-to-use scenarios or alternatives, but the context is clear enough for an agent to select this tool over similar ones.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The set contains many trivially indistinct tools: ai-inference/inference, compress/comprimir, count-tokens/contar-tokens, detect-language/language-detect, and multiple overlapping OCR receipt variants. With 160 tools and pairs that differ only by language or suffix, an agent cannot reliably distinguish several capabilities.
Most names are readable lower-hyphen identifiers, but they mix action verbs, noun phrases, domain prefixes, pipeline suffixes, Spanish/English, and arbitrary demo/batch labels. There is a loose convention, but no consistent verb_noun pattern.
160 tools on one server is an extreme count and clearly unwieldy. Even as a marketplace, exposing every variant, demo, and composed bundle as a top-level MCP tool overwhelms agent selection and adds little distinct capability.
The set covers a huge range of text, image, audio, code, market, compliance, and content-workflow tasks, so many intents have some available tool. However, it is a grab-bag rather than a defined service surface, and the arbitrary demo/specialized variants make it unclear whether a needed operation truly exists or is just a duplicate.