Portuguese Competition MCP

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose: search vs. get, decisions vs. mergers, plus aggregate sector info and metadata. No two tools overlap in functionality or intended use.
Naming Consistency4/5
The consistent pt_comp_ prefix and verb_noun pattern (search_decisions, get_decision, search_mergers, get_merger, list_sectors) make naming predictable. The 'about' tool breaks the verb_noun pattern slightly, and there's a minor singular/plural mismatch between search and get, but these are trivial.
Tool Count5/5
Six tools is an appropriate scope for a focused competition law database: two resource types each with search and get, plus a sector overview and server metadata. Nothing feels redundant or missing.
Completeness5/5
For a read-only legal information server, the surface is complete: it offers both search and retrieval for the two core entity types (decisions and mergers), and list_sectors provides useful aggregate context. No dead ends or essential missing operations.
Average 4/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
This repository is archived. Archived repositories automatically receive an F maintenance tier.
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It only states 'Get', implying a read operation, but does not disclose return format, error behavior, or any permissions. For a simple getter this is sparse, and the agent lacks behavioral context beyond the action itself.
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, front-loaded sentence that is entirely on-topic. Every word contributes to the purpose, with no fluff or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low complexity (one parameter, no nested objects) and the absence of an output schema, the description is minimally adequate. However, it does not mention what the response contains or any caveats, which would be helpful for a getter without structured output metadata.
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 the parameter already described as 'AdC merger case number'. The description does not add any additional meaning beyond the schema, so it meets the baseline without enhancement.
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 gets a specific AdC merger control decision by case number. The verb 'Get' and resource 'merger control decision' are specific, and it distinguishes itself from siblings like pt_comp_get_decision and search tools by focusing on merger decisions and case numbers.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied by the phrase 'by case number' but no explicit alternatives or exclusions are mentioned. It does not say when to prefer this over pt_comp_get_decision or pt_comp_search_mergers, so guidance is minimal.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full transparency burden. It discloses the return fields (case number, parties, outcome, fine amount, Lei da Concorrencia articles) and clearly indicates a search (read) operation, but does not address pagination, rate limits, or data coverage boundaries.
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 two sentences: one for purpose and one for return content. It is front-loaded, specific, and contains no redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the core function and return fields, which is sufficient given the schema handles parameter documentation. It could be more explicit about limitations (e.g., not covering mergers) but overall is complete for a search tool.
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 all five parameters described in the input schema. The description adds no additional parameter-specific meaning beyond what the schema already provides, so the baseline of 3 applies.
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 performs full-text search across AdC enforcement decisions, with specific examples of decision types (abuse of dominance, cartel, sector inquiries). This distinguishes it from sibling tool pt_comp_search_mergers, which focuses on mergers.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for searching enforcement decisions but does not explicitly mention when to use this tool instead of pt_comp_search_mergers or other siblings. No exclusions or alternative tools are named.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the list is filtered to sectors with enforcement activity and includes counts, but does not explicitly state that the operation is read-only or describe any side effects, authentication requirements, or limitations. The description adds some behavioral context but is not exhaustive.
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, front-loaded sentence that states the primary action and the essential output components (sectors, decision counts, merger counts). Every word contributes meaning, with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with no parameters and no output schema, the description adequately conveys the purpose and the expected result. It could potentially mention pagination or sorting, but these are not critical for a sector list. The addition of 'with AdC enforcement activity' clarifies the filter, making it complete enough for typical use.
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?
The tool has zero parameters, so the schema coverage is 100%. Per the rubric, 0 params yields a baseline of 4. The description adds no parameter details because there are none, which is appropriate and does not need to compensate.
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 'List' and resource 'sectors', and clearly distinguishes itself from sibling tools by focusing on sector-level aggregation with decision and merger counts. It is not a tautology and immediately conveys what the tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for sector-level overview, as it lists sectors with activity and counts. However, it does not explicitly state when to use this tool over the search/get sibling tools, nor does it mention alternatives or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does disclose that the tool returns merger cases and names the fields included, which is a useful behavioral trait. However, it does not mention any additional behaviors such as query semantics, ordering, or potential limitations like pagination.
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 sentence that front-loads the main action and resource, then adds relevant return information. It contains zero wasted words and is highly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is relatively simple with four parameters fully described in the schema. The description provides enough context about the tool's purpose and return value, though it does not explain how the search works or how it relates to sibling tools. Overall it is adequate for a basic search tool.
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?
The schema description coverage is 100%, so parameters are already fully documented in structured data. The description does not add any additional meaning to the parameters beyond what the schema provides, so the baseline score of 3 applies.
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's purpose with a specific verb ('Search') and resource ('AdC merger control decisions'), and clarifies the scope ('concentracoes de empresas'). It also lists the key return fields (acquiring party, target, sector, outcome), which distinguishes it from the sibling search tool pt_comp_search_decisions.
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 clearly indicates that this tool is for searching merger control decisions, which gives a clear context for use. It does not explicitly mention when to prefer this over pt_comp_search_decisions, but the specialized focus on mergers makes the intended usage obvious.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It clearly indicates a read-only operation by stating 'Return metadata,' which implies no side effects. It also discloses what specific metadata will be returned. There are no apparent behavioral surprises, making the transparency adequate for a simple metadata tool.
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, concise sentence that immediately states the purpose and enumerates the metadata fields. Every word earns its place, and the structure is perfectly front-loaded with the action verb and resource.
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 trivial complexity (no parameters, no output schema), the description fully covers what the tool does and what it returns. It is complete for its intended purpose and clearly differentiates from sibling tools that access specific data domains.
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?
The tool has zero parameters, and the input schema is empty with 100% coverage (trivially). The baseline for 0 parameters is 4, and the description does not need to add parameter detail since there are none. It adds no param semantics because none are required.
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's purpose: 'Return metadata about this MCP server.' It explicitly lists the metadata components (version, data source, coverage, tool list), using a specific verb ('Return') and resource ('MCP server'). This uniquely distinguishes it from sibling tools that search/get specific records.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool (when server metadata is needed) but does not explicitly state usage context or mention alternatives. No exclusions are provided, but the intended use is fairly obvious. A more explicit statement like 'Use this to understand server capabilities or data coverage' would strengthen it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral burden. It conveys a read-only operation ('Get') and adds context about the case number format (e.g., 'PRC/2023/1'). However, it does not disclose possible error behavior, return format, or any limitations, which is a minor gap for a lookup tool.
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, front-loaded sentence that immediately states the action and resource. It includes useful examples without superfluous words. Every part earns its place.
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 simple getter with one parameter and no output schema, the description provides the essential information: what the tool does, how to specify the case number, and realistic examples. No additional context is necessary given the tool's low complexity.
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?
The schema already describes case_number as 'AdC case number' (100% coverage), but the description adds meaningful examples of the expected format ('PRC/2023/1', 'Ccent/2022/5'). This goes beyond the schema's generic description, helping the agent understand exact input syntax.
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 'Get a specific AdC decision by case number' clearly states the verb (Get), resource (AdC decision), and identifier (case number). It distinguishes itself from sibling tools like pt_comp_search_decisions (searching) and pt_comp_get_merger (mergers) by focusing on fetching a specific decision.
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 implies usage when the user already has a case number, and provides examples of valid formats. It does not explicitly state exclusions or name alternatives, but the context is clear for a simple getter tool. The sibling names (search_decisions) suggest when to use search instead, though this is not explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![portuguese-competition-mcp MCP server](https://glama.ai/mcp/servers/Ansvar-Systems/portuguese-competition-mcp/badges/card.svg)](https://glama.ai/mcp/servers/Ansvar-Systems/portuguese-competition-mcp)

Score Badge

Copy to your README.md:

[![portuguese-competition-mcp MCP server](https://glama.ai/mcp/servers/Ansvar-Systems/portuguese-competition-mcp/badges/score.svg)](https://glama.ai/mcp/servers/Ansvar-Systems/portuguese-competition-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Ansvar-Systems/portuguese-competition-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server