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qune-tech/ocds-mcp

by qune-tech

vergabe-mcp

Local MCP server for German public procurement search. Connects your AI assistant (Claude, GPT, etc.) to the Vergabe Dashboard API for semantic search, tender matching, and company-profile management.

Your queries and company profiles never leave your machine. They are embedded locally with a multilingual ONNX model; only the resulting embedding vectors, OCIDs, and filter values are sent to the API. Data minimisation by design.

Hosted alternative: if you just want search from your AI assistant, there is a hosted MCP connector (OAuth sign-in, no installation) — see the setup guide. This local server is for running the client in your own environment: queries and profiles are embedded locally, only vectors and filter values leave the machine, and it unlocks API-key features.

New to German public procurement? The Vergabe Dashboard knowledge base explains eForms, EU thresholds, and the tender lifecycle, and KI für Vergabe covers the hosted AI side of this server.

Quick Start

1. Get an API key

Sign up at vergabe-dashboard.qune.de and create an API key. API keys require an active Pro plan (€99/month; the local server itself is free and open source — the API gate rides key issuance).

2. Install

Via npx (easiest — downloads the correct binary automatically):

npx @qune-tech/vergabe-mcp --api-key sk_live_YOUR_KEY_HERE

Or download a pre-built binary from GitHub Releases:

Platform

Download

Linux x86_64

vergabe-mcp-linux-x86_64.tar.gz

macOS Apple Silicon

vergabe-mcp-macos-arm64.tar.gz

Windows x86_64

vergabe-mcp-windows-x86_64.zip

Linux / macOS:

# Example for Linux x86_64 — adjust the filename for your platform
tar xzf vergabe-mcp-linux-x86_64.tar.gz
sudo mv vergabe-mcp /usr/local/bin/vergabe-mcp

Windows: Extract the zip and move vergabe-mcp.exe somewhere on your PATH.

Or build from source:

git clone https://github.com/qune-tech/vergabe-mcp.git
cd vergabe-mcp
cargo build --release
# Binary at target/release/vergabe-mcp

3. Configure your AI client

Claude Desktop — edit claude_desktop_config.json:

Using npx:

{
  "mcpServers": {
    "vergabe": {
      "command": "npx",
      "args": ["-y", "@qune-tech/vergabe-mcp", "--api-key", "sk_live_YOUR_KEY_HERE"]
    }
  }
}

Using the binary directly:

{
  "mcpServers": {
    "vergabe": {
      "command": "vergabe-mcp",
      "args": ["--api-key", "sk_live_YOUR_KEY_HERE"]
    }
  }
}

Claude Code — add .mcp.json to your project root:

Using npx:

{
  "mcpServers": {
    "vergabe": {
      "command": "npx",
      "args": ["-y", "@qune-tech/vergabe-mcp", "--api-key", "sk_live_YOUR_KEY_HERE"]
    }
  }
}

Using the binary directly:

{
  "mcpServers": {
    "vergabe": {
      "command": "vergabe-mcp",
      "args": ["--api-key", "sk_live_YOUR_KEY_HERE"]
    }
  }
}

Cursor — Settings → MCP Servers → Add:

Using npx:

  • Command: npx

  • Args: -y @qune-tech/vergabe-mcp --api-key sk_live_YOUR_KEY_HERE

Using the binary directly:

  • Command: vergabe-mcp

  • Args: --api-key sk_live_YOUR_KEY_HERE

LM Studio — Settings → MCP → Add Server:

  1. Click + Add Server and choose STDIO

  2. Fill in:

Using npx:

  • Name: vergabe

  • Command: npx

  • Arguments: -y @qune-tech/vergabe-mcp --api-key sk_live_YOUR_KEY_HERE

Using the binary directly:

  • Name: vergabe

  • Command: full path to the binary, e.g. /usr/local/bin/vergabe-mcp

  • Arguments: --api-key sk_live_YOUR_KEY_HERE

  1. Click Save

  2. In the chat, select a model that supports tool use and enable the vergabe server

LM Studio requires models with tool-calling support (e.g. Qwen 2.5, Mistral, Llama 3.1+). Smaller models may not use all 11 tools reliably — 7B+ recommended.

Replace sk_live_YOUR_KEY_HERE with your actual API key. You can also pass the key via the VERGABE_API_KEY environment variable.

Related MCP server: peru-gob-mcp

Available Tools (11)

Tool

Description

search_text

Semantic search across all tenders (query embedded locally)

list_releases

Filter and browse tenders by phase, CPV prefix, country, value range, deadline, buyer, procurement method

get_release

Raw eForms XML envelope for one OCID (optional notice_id selects a sibling)

linked_notices

A procurement's notice lineage (PIN→CN→CAN) as {ocid, notice_id} refs

get_index_info

API health/version, embedder status, and embedding-contract check

create_company_profile

Create a matching profile for your company (stored locally)

update_company_profile

Update an existing profile

get_company_profile

View profile details

list_company_profiles

List all your profiles

delete_company_profile

Delete a profile

match_tenders

Match a profile against all tenders by semantic similarity (vector embedded locally)

CLI Options

Usage: vergabe-mcp [OPTIONS]

Options:
      --db <DB>            Local profiles database [default: profiles.db]
      --data-dir <DIR>     Data directory [default: data]
      --api-url <URL>      Vergabe Dashboard API [default: https://vergabe-dashboard.qune.de]
      --api-key <KEY>      API key [env: VERGABE_API_KEY]
  -h, --help               Print help

How It Works

LLM ←stdio→ vergabe-mcp (local)
               │  Local: company profiles (SQLite) + sentence embedder (ONNX)
               │  HTTPS: vectors, OCIDs, filter values, API key
               └──HTTPS──→ Vergabe Dashboard API (/api/v1)

The MCP server runs locally on your machine:

  • Company profiles (name, description, CPV interests, location) are stored in a local SQLite database — they never leave your network.

  • Text embeddings are computed locally with a multilingual ONNX model (multilingual-e5-small, 384-dim). Search queries use the query: prefix and go to POST /api/v1/search/vector; profile descriptions use the passage: prefix and go to POST /api/v1/match/vector.

  • Only embedding vectors (384 floats), the OCIDs you fetch, filter values, and your API key are sent to the API. Your query and profile text stay local.

Privacy & data flow

What stays on your machine: profile text, search-query text, CPV interests, location.

What the API sees: embedding vectors, the OCIDs you read, the filter values you use, and your API key. These reveal commercial interest (which sectors, buyers, value bands, tenders you look at) but not the underlying text. Embedding vectors are a derived, pseudonymous representation — minimisation, not elimination.

First-run model download: on first use the server downloads the embedding model from huggingface.co:

  • What: model.onnx + tokenizer.json, ~118 MB total.

  • When: the first time the embedder runs; cached afterwards at ~/.cache/vergabe/models/multilingual-e5-small.

  • From: huggingface.co (a US-operated third party). No user data is sent in this fetch — it is a plain model download.

For air-gapped / enterprise installs, place model.onnx and tokenizer.json in the cache directory above and the runtime download is skipped.

Requirements

  • An API key from vergabe-dashboard.qune.de on the Pro plan (€99/month)

  • ~120 MB disk space for the ONNX model (downloaded automatically on first run)

  • Internet connection to reach the API

License

MIT — see LICENSE.

Available Tools

10 tools
create_company_profileA

Create a company profile for tender matching. Store company name, description, CPV codes, categories, and location. The description is automatically embedded for semantic matching if the embedder is loaded. Returns the profile ID and embedding status.

ParametersJSON Schema
NameRequiredDescriptionDefault
categoriesNoProcurement categories of interest (e.g. ['works', 'services', 'goods'])
cpv_codesNoCPV codes the company is interested in (e.g. ['45000000', '72000000'])
descriptionYesDescription of the company's activities, products, and services. German text recommended for best matching quality.
locationNoCompany location (e.g. 'Berlin, Germany')
nameYesCompany name

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Describes automatic embedding behavior and return of profile ID and embedding status. No annotations provided, so description carries burden; it's informative but lacks error conditions or idempotency details.

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?

Three concise sentences, front-loaded with main action. 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?

Covers purpose, stored data, behavioral side effect, and return values. Lacks details on required auth or error handling, but adequate for a create tool.

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?

Schema coverage is 100%, but description adds value: explains that description is auto-embedded and recommends German text. This goes beyond schema definitions.

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?

Clearly states the tool creates a company profile for tender matching, listing stored fields (name, description, CPV codes, categories, location) and mentioning automatic embedding. Distinguishes from sibling tools like delete, get, update, match_tenders.

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?

Implies usage for creating profiles for tender matching. Recommends German text for better quality, indicating context of use. No explicit when-not or alternatives, but purpose is self-evident.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

delete_company_profileA

Delete a company profile and its embedding by ID.

ParametersJSON Schema
NameRequiredDescriptionDefault
idYesThe UUID of the company profile to delete

TDQS

A3.5/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden. It mentions embedding deletion, which is useful, but fails to disclose if deletion is irreversible, requires authorization, or returns output. The insight about embeddings adds value but is insufficient.

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, front-loading the core action and specific scope in a single sentence with no extraneous content.

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?

For a simple delete operation with one parameter, the description is adequate but incomplete. No output schema, no annotations, and missing details about return values or error handling. It covers the basic function but not the full context.

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 'id' already described. The description adds minimal value beyond 'by ID', aligning with the schema. Baseline score 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 clearly states the action (delete), the target resource (company profile), and the specific scope (including its embedding), distinguishing it from sibling tools like get, list, update, and create.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives, such as prerequisites (e.g., ensuring no dependencies) or potential consequences. The description lacks context for appropriate invocation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_company_profileA

Get a company profile by its ID. Returns the full profile including name, description, CPV codes, categories, location, and embedding status.

ParametersJSON Schema
NameRequiredDescriptionDefault
idYesThe UUID of the company profile to retrieve

TDQS

A3.8/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It lists return fields but does not explicitly state that the operation is read-only, require any authentication, or disclose other behavioral traits.

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?

Two sentences with no wasted words. The first sentence states the core action, and the second lists key return fields. Front-loaded and efficient.

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 retrieval tool with one parameter and no output schema, the description is sufficiently complete, covering purpose and return content. Minor gaps include missing error scenarios or authentication needs.

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 a clear parameter description. The tool description adds no extra meaning beyond 'by its ID', maintaining a baseline of 3.

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 action ('Get') and the resource ('company profile by its ID'), and distinguishes from siblings like list_company_profiles (list vs single) and create/update/delete by being a read operation.

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 retrieving a single profile by ID but offers no explicit guidance on when to use this tool versus alternatives like search_text, nor does it mention any prerequisites or limitations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_index_infoA

Get combined statistics: release and embedding counts from the REST API, plus local company profile counts. Call this first to check connectivity and see what data is available.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A4.3/5.0
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 indicates a read operation and mentions data sources (REST API, local), but does not disclose potential side effects, authentication needs, or rate limits. It is moderately transparent.

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, front-loaded with the core purpose, and every sentence adds value. No unnecessary words.

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 context of zero parameters, no output schema, and many sibling tools, the description fully explains what the tool does and its role as an initial connectivity and data availability check. It is complete and appropriately scoped.

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 schema coverage is 100%. The description does not need to add parameter details, and it appropriately adds value by explaining what the tool returns and its purpose.

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 verb 'Get' and the resource 'combined statistics', detailing what is included: release and embedding counts plus local company profile counts. It distinguishes itself from sibling tools which are CRUD operations on specific entities.

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 advises to call this tool first to check connectivity and see available data, providing clear guidance on when to use it. While it doesn't mention when not to use it or name alternatives, the usage context is well defined.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_releaseA

Get the full OCDS release data for a specific tender by its OCID (Open Contracting ID). Returns the complete release JSON including tender details, buyer, parties, awards, and lot structure. Data is fetched from the REST API.

ParametersJSON Schema
NameRequiredDescriptionDefault
ocidYesThe OCID (Open Contracting ID) of the release to retrieve

TDQS

A3.6/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It mentions the output includes specific fields and that data comes from a REST API, but lacks details on error handling, authentication, or rate limits.

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 with two sentences. The first clearly states the purpose, and the second adds useful output details. No extraneous 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?

Given no output schema, the description adequately explains the return value (complete release JSON with key fields). It could mention error cases, but overall it provides sufficient context for a simple retrieval 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% for the single parameter 'ocid', and its schema description adequately explains it. The tool description adds no extra semantic value beyond the schema.

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: retrieving the full OCDS release data for a specific tender by OCID. It distinguishes from sibling tools like list_releases, which likely returns multiple records.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. For example, it doesn't specify that users need an OCID, or contrast it with list_releases for broader queries.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_company_profilesA

List all company profiles. Returns a summary of each profile (without description) ordered by creation date.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description discloses key behaviors: returns summaries without descriptions, ordered by creation date, and implies it is a read-only listing operation. However, it lacks details on pagination, authentication needs, or rate limits.

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 efficiently conveys purpose and return characteristics with no redundant words.

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?

Given no output schema, the description adequately explains the return value (summary, without description, ordered). However, it lacks mention of whether the result is paginated or truncated, which would be helpful for completeness.

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?

No parameters exist, so schema coverage is 100%. The description adds no parameter-specific meaning, but this is acceptable as the tool has no inputs; baseline 4 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 'List all company profiles' with a specific verb and resource, and distinguishes itself from sibling tools like get_company_profile by noting it returns only summaries without descriptions and ordered by creation date.

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 listing all profiles but provides no explicit guidance on when to use this tool over alternatives like get_company_profile or search_text, nor does it mention any exclusions or prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_releasesA

List and filter OCDS procurement releases by structured criteria: month, CPV code prefix, procurement category, procurement method, value range, and buyer name. Supports pagination. Data is fetched from the REST API. Use this for tender discovery before drilling into specific releases with get_release.

ParametersJSON Schema
NameRequiredDescriptionDefault
buyer_nameNoFilter by buyer name (case-insensitive substring match)
cpv_prefixNoFilter by CPV code prefix (e.g. '45' for construction, '72' for IT)
deadline_afterNoOnly include tenders with deadline on or after this ISO-8601 datetime
deadline_beforeNoOnly include tenders with deadline on or before this ISO-8601 datetime
eu_fundedNoFilter by EU funding status (true = EU funded)
has_awardsNoFilter by whether the release has awards with suppliers (true/false)
limitNoMaximum number of results (default 20, max 200)
location_nutsNoFilter by delivery location NUTS code prefix (e.g. 'DE3' for Berlin)
main_procurement_categoryNoFilter by main procurement category (e.g. 'works', 'goods', 'services')
monthNoFilter by month in YYYY-MM format
nuts_codeNoFilter by NUTS code prefix (e.g. 'DE2' for Baden-Württemberg)
offsetNoOffset for pagination (default 0)
procurement_methodNoFilter by procurement method (e.g. 'open', 'selective', 'limited')
result_codeNoFilter by result code (e.g. 'selec-w' for selected winner, 'clos-nw' for closed no winner)
statusNoFilter by tender status (e.g. 'active', 'complete', 'cancelled')
submission_deadline_afterNoOnly include tenders with submission deadline on or after this ISO-8601 datetime
submission_deadline_beforeNoOnly include tenders with submission deadline on or before this ISO-8601 datetime
tagNoFilter by lifecycle tag (e.g. 'tender', 'award', 'planning')
value_maxNoMaximum tender value
value_minNoMinimum tender value

TDQS

A3.6/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must fully disclose behavior. It mentions pagination and data source ('fetched from the REST API') but fails to discuss response format, defaults, error handling, rate limits, or whether the operation is read-only. Given the 20-parameter complexity, this is insufficient for an agent to safely invoke the 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 three concise sentences, each carrying essential information: purpose and filters, pagination and data source, and usage guidance. No fluff or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 20 parameters, no output schema, and no annotations, the description is too sparse. It does not clarify how filters combine (AND/OR), default sorting or ordering, response structure, or error behavior. The agent would lack critical context for correct invocation.

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%, meaning every parameter has a description in the input schema. The tool description only summarizes a subset of filters without adding new meaning, so the 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 clearly states the verb 'List and filter' and the resource 'OCDS procurement releases', specifies several filtering criteria (month, CPV prefix, etc.), and explicitly distinguishes from the sibling tool `get_release` by stating it is for 'tender discovery before drilling into specific releases'.

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 explicit guidance: 'Use this for tender discovery before drilling into specific releases with get_release.' This clarifies the primary use case and indicates an alternative tool for detailed information, though it does not list when NOT to use it or mention any prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

match_tendersA

Match a company profile against tenders using semantic similarity. The profile's embedding is sent to the REST API for KNN cosine search against all tender chunks. Results are enriched with release metadata from the API and can be post-filtered by CPV prefix, category, method, value range, buyer name, deadline, and status. Deduplicates by OCID (keeps highest score).

ParametersJSON Schema
NameRequiredDescriptionDefault
buyer_nameNoFilter by buyer name (case-insensitive substring match)
cpv_prefixNoFilter by CPV code prefix (e.g. '45' for construction, '72' for IT)
deadline_afterNoOnly include tenders with deadline on or after this ISO-8601 datetime
deadline_beforeNoOnly include tenders with deadline on or before this ISO-8601 datetime
eu_fundedNoFilter by EU funding status (true = EU funded)
has_awardsNoFilter by whether the release has awards with suppliers (true/false)
kNoNumber of matching tenders to return (default: 10)
location_nutsNoFilter by delivery location NUTS code prefix (e.g. 'DE3' for Berlin)
main_procurement_categoryNoFilter by main procurement category (e.g. 'works', 'goods', 'services')
procurement_methodNoFilter by procurement method (e.g. 'open', 'selective', 'limited')
profile_idYesThe UUID of the company profile to match against tenders
statusNoFilter by tender status (e.g. 'active', 'complete', 'cancelled')
tagNoFilter by lifecycle tag (e.g. 'tender', 'award', 'planning')
value_maxNoMaximum tender value
value_minNoMinimum tender value

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden. It explains the algorithm (semantic similarity, KNN search, enrichment, dedup), but does not disclose read-only nature, auth, or rate limits. The behavior is well-described but some aspects are missing.

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?

Three sentences, front-loaded with the core action. Each sentence is informative and necessary: method, filters, dedup. No redundancy.

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?

Given 15 parameters and no output schema, the description explains the matching algorithm and filtering but does not describe the return format or enriched metadata contents. Still fairly complete for a complex 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%, so baseline is 3. The description adds context about post-filtering but does not significantly enhance parameter meaning beyond the schema descriptions.

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 it matches a company profile against tenders using semantic similarity, describes the process (embedding, KNN cosine search, enrichment, filtering, deduplication), and distinguishes itself from sibling tools like search_text and list_releases.

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 context for use (finding semantically similar tenders to a profile) and lists extensive filtering options, but does not explicitly state when not to use it or name alternative tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

search_textA

Search tenders by text query. The query is embedded locally using multilingual-e5-small and matched against tender chunks via cosine similarity on the REST API. German text works best for matching German procurement data.

ParametersJSON Schema
NameRequiredDescriptionDefault
kNoNumber of results to return (default: 10)
queryYesText query to search for (German works best). The query is embedded locally and matched against tender chunks via cosine similarity.

TDQS

A3.9/5.0
Behavior4/5

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 the embedding model ('multilingual-e5-small'), the matching method ('cosine similarity'), and language preference. However, it does not mention pagination, what happens with k=0, or output format, leaving some behavioral gaps.

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?

Two sentences, no filler. The purpose is front-loaded, and every sentence adds value. Highly concise and well-structured.

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?

The tool is low complexity with 2 params, but no output schema exists. The description does not explain the return format (e.g., list of tenders with scores), which would help completeness. Adequate but could be improved with output details.

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 descriptions for both parameters. The tool description adds context about the embedding and matching process, but this is general, not parameter-specific. For the 'k' parameter, no extra meaning is added beyond the schema's default note. Baseline 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 clearly states the verb ('Search') and resource ('tenders by text query'), and distinguishes from siblings by specifying the use of 'multilingual-e5-small' embedding and cosine similarity. This technical detail helps differentiate it from other search or match tools.

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 provides a language hint ('German text works best') but does not explicitly state when to use this tool versus alternatives like 'match_tenders'. No exclusion criteria or context for optimal use are given, so usage guidance is implied rather than explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

update_company_profileA

Update a company profile. Only provided fields are changed. If the description changes, the profile is automatically re-embedded.

ParametersJSON Schema
NameRequiredDescriptionDefault
categoriesNoNew procurement categories (replaces existing)
cpv_codesNoNew CPV codes (replaces existing)
descriptionNoNew description
idYesThe UUID of the company profile to update
locationNoNew location. Use empty string to clear.
nameNoNew company name

TDQS

A3.7/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses partial update behavior and that description changes trigger auto-re-embedding. However, no annotations exist, so the description carries the full burden; it lacks details on side effects, auth requirements, or idempotency.

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?

Two sentences, no wasted words. The purpose is stated first, followed by a critical behavioral note. Efficient and front-loaded.

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?

Adequate for basic functionality but lacks context on error handling (e.g., non-existent id), return value (no output schema), and any additional constraints. The re-embedding detail is a plus.

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?

Schema coverage is 100%, but the description adds value by explaining partial update semantics and noting that empty string clears the location field. This goes beyond the schema's parameter descriptions.

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 'Update a company profile' with the specific verb 'update' and resource 'company profile'. It distinguishes from sibling tools like create, delete, get, list.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives like create_company_profile or delete_company_profile. The phrase 'Only provided fields are changed' implies partial update but does not clarify prerequisites or scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 10 tool updatesv1.0.0
    • First observedcreate_company_profile
    • First observeddelete_company_profile
    • First observedget_company_profile
    • First observedget_index_info
    • First observedget_release
    • First observedlist_company_profiles
    • First observedlist_releases
    • First observedmatch_tenders
    • First observedsearch_text
    • First observedupdate_company_profile

TDQS

A4.1/5.0

Scored across 10 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: CRUD for company profiles, listing/fetching releases, text search, semantic matching, and index info. No overlap in functionality.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (e.g., create_company_profile, list_releases, match_tenders). No mixing of styles.

Tool Count5/5

10 tools is well-scoped for the domain of company profile management and OCDS tender matching. Each tool serves a necessary function without redundancy.

Completeness5/5

The tool set covers full CRUD for company profiles, comprehensive tender discovery (listing, filtering, text search, semantic matching), and metadata about indexes. No obvious gaps for the intended use case.

Maintenance

ActivitySlowing
ResponsivenessNo issues

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