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gapup-mcp

by io.github.getgapup

gov_procurement_multi

Read-only

Aggregate public procurement tenders (calls for tender / appels d'offres) from multiple government sources simultaneously: TED Europa v3 (27 EU countries, keyless API), BOAMP France (opendatasoft, keyless), UK Contracts Finder (OCDS standard, keyless), SAM.gov United States (requires SAM_GOV_API_KEY env var), and bund.de Germany (HTML scraping, partial). Returns structured tender records with buyer authority, EU CPV sector code, estimated contract value converted to EUR via live FX rates, submission deadlines, and direct notice URLs. Use when: a B2G agent needs to find government contract opportunities matching keywords across multiple jurisdictions; building a pipeline of public tenders for bid/no-bid qualification; monitoring a domain by CPV code; market sizing public sector spend. Key inputs: query (keywords), countries (ISO-2 array), cpv_codes (EU standard codes, e.g. 72000000=IT services, 45000000=construction, 79000000=business services), min_value_eur (filter), published_after (ISO date, defaults to 30 days ago). SLA: <=25s p95 (all sources fetched in parallel, 8s budget per source). Optional env var SAM_GOV_API_KEY enables US federal tenders (free key at api.sam.gov). Quality score: 25 pts if TED EU retrieved, 15 pts per other source retrieved (max 60), 10 pts if >= 10 tenders returned, 5 pts if aggregates computed. Status: failed < 30 / partial 30-59 / final >= 60.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asyncNoIf true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
queryYesKeywords to search for tenders (e.g. "cybersecurity audit", "construction", "consulting AI")
countriesNoCountries to search. Defaults to ["EU","US","FR","UK","DE"]. Use "EU" for all 27 EU member states via TED Europa.
cpv_codesNoEU Common Procurement Vocabulary codes (e.g. ['72000000'] for IT services, ['45000000'] for construction). Optional.
min_value_eurNoMinimum contract value in EUR. Tenders below this are excluded. Optional.
published_afterNoISO date YYYY-MM-DD. Only return tenders published after this date. Defaults to 30 days ago.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
statusYes
sourcesYes
tendersYes
by_sourceYes
by_countryYes
quality_scoreYes
countries_searchedYes

TDQS

A4.9/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, destructiveHint), the description discloses parallel fetching, per-source time budgets (8s), a quality score formula, partial/final status grades, optional SAM_GOV_API_KEY requirement, and bund.de's partial coverage. This operational detail materially exceeds the annotation baseline.

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 long but efficiently organized with clear sections: function, source list, use cases, key inputs, SLA, and quality scoring. Every sentence provides operational or selection-relevant information without 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?

Despite the tool's complexity (6 params, 5 sources, async mode), the description covers output fields (buyer authority, CPV, value in EUR, deadlines, URLs), failure grading (failed/partial/final), environmental prerequisites (env var), and performance expectations. This is sufficient for an agent to invoke and interpret results.

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%, so baseline is 3. The description adds value by giving concrete CPV code examples (72000000=IT services, 45000000=construction), clarifying the ISO-2 array format, and reaffirming defaults for countries and published_after. It also explains quality scoring that depends on source coverage.

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 aggregates public procurement tenders from multiple named government sources (TED, BOAMP, UK Contracts Finder, SAM.gov, bund.de) with a specific verb 'Aggregate'. It also lists concrete use cases (B2G lead finding, pipeline building, CPV monitoring, market sizing) distinguishing it from siblings like rfp_tender_architect or procurement_spend_optim.

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

Usage Guidelines5/5

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

An explicit 'Use when' section provides four distinct scenarios with example keywords and CPV usage. It also includes SLA and quality score semantics, enabling the agent to decide if this tool fits the task and to interpret partial results.

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

C2.4/5.0
Disambiguation1/5

Over 50 tools share the identical template 'Gapup agent-payable C-suite expertise' with similar French descriptions and reference cases, making their boundaries indistinguishable. Clusters like competitor_intel, competitive_deep_dive, competitor_moves, competitor_profiles, competitor_pricing_radar, competitor_pricing_scrape, and competitor_recommendations heavily overlap in purpose.

Naming Consistency1/5

Names are chaotic: mix of French and English, snake_case and camelCase, verb_noun, noun, and adjective forms with no uniform pattern. Examples like 'bp_narratif', 'content_enrichment', 'ai_governance_full_report_async', and 'job_result' show no coherent naming convention.

Tool Count1/5

271 tools is far beyond any reasonable MCP server scope, creating an overwhelming selection burden for agents. This count vastly exceeds the 25+ threshold for 'too many' and makes navigation impractical.

Completeness2/5

While the server covers many business domains, it lacks lifecycle operations (e.g., no update/delete tools for the deliverables it generates) and the input specifications are vague ('documented case fields' without documentation), creating functional dead ends. The sheer breadth does not compensate for these gaps.

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