Extrait des champs structures (texte, nombre, date) depuis un texte libre.
Extrait des champs structures (texte, nombre, date) depuis un texte libre. Taux de reussite mesure publie.
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are empty, so the description carries the full burden. It reveals only that extraction has a 'published measured success rate,' hinting at possible non-determinism, but it does not disclose failure behavior, output format, rate limits, or any side effects. This is insufficient for a tool invoked by an autonomous agent.
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 short, with the main behavior front-loaded and no redundant filler. The second sentence is terse and somewhat incomplete, but it does add a useful behavioral hint without bloating the definition.
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?
Given the generic input schema, missing output schema, and empty annotations, the description is too thin. It fails to specify the concrete input JSON structure, the result shape, or the actual measured success rate, leaving an agent with only a vague sense of how to call the tool correctly.
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 description coverage is 0%, so the description must explain the 'input' parameter. It does indicate that the relevant content is free text and lists the field types extracted, but it does not clarify the actual object shape required by the schema (propertyNames/additionalProperties). An agent still cannot confidently construct the input object.
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 states a clear action ('Extrait des champs structures') and a resource ('depuis un texte libre'), and even enumerates the extraction types: text, number, and date. It is specific enough for an agent to know roughly what the tool does, and there are no sibling tools that require differentiation.
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 intended use case is implied: use this tool when you need to pull structured fields out of free text. However, there is no explicit guidance on when not to use it, no prerequisites, and no mention of alternatives, though the absence of sibling tools lowers the need for those exclusion rules.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
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Turn messy text into strict JSON schemas agents can trust (invoice, receipt, contact, resume).
Extract structured data points from research papers and other documents with an LLM.
High-fidelity PDF to structured Markdown conversion and document field extraction.
Agent-native document parsing: PDF, scans and FR/EU invoices to structured JSON or Markdown.
Add one secure layer between your agents and this server.
With only one tool, there is no possibility of confusion or misselection. The tool's purpose is clearly defined and distinct by default.
The single tool name 'run' is consistent simply because there are no other names to conflict with it. However, it is a generic verb that lacks the domain-specific clarity expected from an extraction-focused server, so a small deduction is warranted.
The server has exactly one tool, which is slightly below the typical 3-15 range but appropriate for its narrow, single-purpose scope of extracting structured fields from text. It feels minimal yet does not seem artificially inflated or overly sparse.
The tool fully covers the server's stated purpose of extracting text, number, and date fields from free text. There are no apparent missing operations needed for this focused domain; the single action is sufficient and has no dead ends.
