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ukintel.uk — UK company, tender and grant intelligence

Score live tenders against a supplier profile, 0-100 with per-factor breakdown

uk_tenders_fit_score

Send a supplier profile (what you do, keywords, regions, value band, certifications) and get the best-fitting live UK tenders, each scored 0-100 by deterministic published rules across five factors - relevance, value fit, geography, timeline, buyer history - each with its numerator AND denominator so you can re-weight client-side, plus a what-to-emphasise note on the top matches. Rules score; a model never does.

Costs $0.20 USDC per call (x402, Base mainnet). Returns: result = { considered, matches[] of { ...notice brief..., fit: { score, band strong|possible|weak, factors{ relevance/of, value_fit/of_v, geography/of_g, timeline/of_t, buyer_history/of_b }, flags[], rules_version }, emphasise } }.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topNo
filtersNoOptional pre-filters (same fields as /v1/tenders/search).
supplierYesThe supplier profile to match against live tenders.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.1/5.0
Behavior5/5

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

With no annotations, the description carries the full disclosure burden and does so well. It reveals that scoring is deterministic and rule-based, not model-based, that each call costs $0.20 USDC, that per-factor numerators/denominators are returned for client-side re-weighting, and it details the result structure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense and information-rich, with useful details like cost, determinism, and output shape. It is longer than strictly necessary due to the nested result structure, but every major sentence adds value and the core purpose is front-loaded.

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 complex tool with no output schema and no annotations, the description is remarkably complete. It explains what results look like, how scoring works, what factors are included, and even monetization. An agent has enough context to invoke it and interpret the response.

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 67%, so the baseline is 3. The description lists profile fields like keywords, regions, value band, and certifications, but adds little meaning beyond their names. It does not explain top or filters any further than the schema already does.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action: send a supplier profile and receive best-fitting live UK tenders scored 0-100. It is distinct from a generic search tool by emphasizing profile-based matching, but it does not explicitly name a sibling alternative such as uk_tenders_search.

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 makes the intended use case clear: matching a supplier profile to live tenders. It does not explicitly state when not to use it or name alternatives, but the profile-driven scoring context is sufficient to guide selection.

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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