opportunity-exchange

query_knowledge_graph

Query the materialized semantic graph without downloading whole datasets. Resolve an entity name/code and optional municipality geography, then return typed facts and relationships. This supports municipality-by-NAICS GDP and business queries without encoding classifications in metric names. Missing facts or edges mean unknown, never zero or false.

Input Schema

TableJSON Schema

Name	Required	Description
`type`	No	Optional entity-type constraint.
`depth`	No	Hops to expand outward from entity; default 1. Above 1 adds an expansion block of nodes and edges annotated with the hop that reached them, so a containment chain such as municipality -> census-division -> economic-region can be fetched in one call instead of three. Requires entity.
`limit`	No	Maximum facts, relationships, or listed entities; default 25.
`bundle`	No	Bounded metric preset. municipal-economic-profile-v1 requires one municipality entity.
`entity`	No	Exact name, slug, graph id, NOC code, SGC code, source id, or provider id.
`metrics`	No	Fact metrics, e.g. population.
`maxNodes`	No	Ceiling on entities across the whole expansion; default 200. Distinct from limit, which bounds one level — without it a depth of 2 from a municipality would pull every business in it.
`geography`	No	Optional exact municipality name, slug, or SGC code for geography-scoped facts.
`predicates`	No	Relationship predicates, e.g. classified_as or serves.
`truthStates`	No
`minimumConfidence`	No
`referencePeriodEnd`	No	Inclusive ISO-8601 upper bound for fact reference periods.
`referencePeriodStart`	No	Inclusive ISO-8601 lower bound for fact reference periods.

TDQS

A4.1/5.0

Behavior4/5

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

With no annotations, the description carries the burden and adds important behavioral traits: it queries a materialized graph, returns typed facts/relationships, and explicitly states that missing facts or edges mean 'unknown, never zero or false.' It does not mention rate limits or auth, but the read-only query semantics and unknown-value rule are valuable.

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?

Four sentences, each earning its place: purpose, operation, use case, and a key semantic caveat. Information is front-loaded and no filler is present.

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 13-parameter tool with no output schema or annotations, the description provides a helpful orientation but not complete context. It lacks explicit guidance on response structure, pagination/limits behavior, and when to prefer sibling tools like traverse_relationships or get_entity.

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 85%, so the schema already documents most parameters. The description adds the conceptual linking of entity/geography to the returned facts and relationships, but it does not enrich individual parameter semantics beyond what the schema provides.

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 ('Query') and identifies the resource ('materialized semantic graph'), then details the operation: resolve an entity name/code and optional municipality geography to return typed facts and relationships. It also distinguishes itself from sibling tools by emphasizing graph queries without downloading datasets and without encoding classifications in metric names.

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?

It provides clear usage context: this tool is for municipality-by-NAICS GDP and business queries and for querying the graph instead of downloading whole datasets. It does not explicitly name alternatives or state when not to use it, so it misses the full exclusion guidance.

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

B3.1/5.0

Disambiguation1/5

Several tools are effectively duplicates or near-duplicates: get_emerging_signals is an explicit alias for list_economic_signals, resolve_entity and resolve_entities overlap heavily, and the markdown variants duplicate their non-markdown reports. Pairs like compare_communities/compare_municipalities and search_businesses/search_licensed_businesses also require reading long contracts to avoid misselection.

Naming Consistency4/5

Tool names overwhelmingly follow a snake_case verb_noun pattern with sensible verbs like get_, list_, search_, and compare_. The main deviations are the backwards-compatible get_emerging_signals alias and prefix choices such as check_business_health vs get_business_health that obscure the underlying distinction.

Tool Count1/5

70 tools is an extreme surface for any MCP server, far beyond the 25+ 'too many' threshold. The set is fragmented by format variants, aliases, and multiple overlapping lookup tools, making selection and maintenance costly.

Completeness3/5

The domain surface is broad: entity resolution, business health, labour, community economy, procurement, and governance evidence are all covered in depth. However, there are notable lifecycle gaps—no sandbox deletion, consent revocation, health-action cancellation, or actual exchange/connect/apply step—that leave agents with dead ends.

Resources

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