A connector providing AI assistants searchable access to climate-aligned contract clauses, glossary terms, and practical guides from The Chancery Lane Project's curated knowledge graph.
Find TCLP content nodes (clauses, glossary terms) associated with a named concept.
Unlike `search`, this performs a deterministic name match against Entity nodes in
the knowledge graph rather than a relevance-ranked semantic search. Use it when
you have a specific term or concept (e.g. "scope 3 emissions", "net zero") and
want to retrieve every clause or glossary entry that explicitly references it.
Args:
name: The entity or concept name to look up (exact match, case-insensitive).
limit: Maximum number of results to return (1–50).
include_full_text: Include each hit's full body text (Markdown). Off by
default — bodies are large; request only when you need the content,
and prefer a small `limit` when you do.
Returns:
JSON with "meta" and "results" where each hit includes the source content
node and the entity names that matched.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | ||
| limit | No | ||
| include_full_text | No |
| Name | Required | Description |
|---|---|---|
| meta | Yes | |
| results | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, openWorldHint=true, and destructiveHint=false. The description adds valuable behavioral context: deterministic match, case-insensitive exact matching, and a warning that include_full_text bodies are large. It does not contradict annotations and provides useful extra details for the agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured: a concise purpose statement, a clear differentiation from search, usage guidance, and a bullet-like parameter list. Every sentence adds value without redundancy. It is front-loaded with the most important information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's three parameters, the presence of annotations, and an output schema, the description is complete. It explains the deterministic nature, the return format (JSON with meta and results), and provides parameter usage advice. The output schema is not detailed in the description, but that is acceptable since it exists separately.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must carry the burden. It does so excellently: for 'name' it specifies 'exact match, case-insensitive'; for 'limit' it states 'Maximum number of results to return (1–50)'; for 'include_full_text' it explains the default off, the size concern, and recommends using a small limit when requesting full text. This adds significant meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool finds TCLP content nodes (clauses, glossary terms) associated with a named concept. It distinguishes itself from the sibling 'search' by specifying it performs a deterministic name match rather than a relevance-ranked semantic search, which provides clear differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use it when you have a specific term... and want to retrieve every clause or glossary entry that explicitly references it' and contrasts with 'search'. However, it does not mention when to use or avoid the other sibling tools (taxonomy_content, taxonomy_facets), leaving some ambiguity for those alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search the TCLP knowledge graph using fusion search (semantic + BM25).
Args:
query: Free-text search query (max 1000 characters).
node_type: Content scope — "tclp" (clauses, glossary terms, guides),
"lrsf" (laws, regulations, standards, frameworks), or "all".
limit: Maximum number of results to return (1–50).
rerank: Whether to apply RRF reranking when combining graph and text results.
include_full_text: Include each hit's full body text (Markdown). Off by
default — bodies are large; request only when you need the content,
and prefer a small `limit` when you do.
Returns:
JSON with "meta" (totals, timing) and "results" (ranked hits with title,
url, content_type, scores, and optionally relationships and full_text).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | ||
| rerank | No | ||
| node_type | No | all | |
| include_full_text | No |
| Name | Required | Description |
|---|---|---|
| meta | Yes | |
| results | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows it's safe. The description adds valuable behavioral context: mentions fusion search, RRF reranking, and warns that bodies are large, which goes beyond annotation hints. No contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is well-structured with Args section and Returns section, each parameter documented succinctly. Every sentence adds value, and the warning about body size is concise yet crucial. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 5 parameters, 0% schema coverage, and available output schema, the description fully explains all parameters, their defaults, and usage nuances. The return format is described (JSON with meta and results), and the output schema can handle details. Completely adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate fully. It does: defines query as free-text (max 1000 chars), node_type as content scope with specific vocab, limit as 1–50, rerank as RRF switching, and include_full_text with a usage warning. This adds significant meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches the TCLP knowledge graph using fusion search (semantic + BM25), which is a specific verb+resource combination. It distinguishes from siblings like entity_lookup and taxonomy queries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use this tool (for full-text search) and includes a practical note on include_full_text (request only when needed, prefer small limit). However, it does not explicitly state when NOT to use it or point to alternatives for specific use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List TCLP content (clauses, guides) filtered by taxonomy facet=value.
Multi-facet filters are combined with AND. Call `taxonomy_facets` first
to learn which facet names and slugs exist; using a name not in that
list returns a 400.
Args:
filters: Mapping of facet name to value slug, e.g.
`{"sector": "real-estate", "practice_area": "commercial"}`.
Each facet may appear at most once.
scope: `clause`, `guide`, or `all` (default).
limit: Maximum results to return (1–100, default 25).
offset: Result offset for paging (default 0).
sort: `title`, `date_published_desc`, or `date_modified_desc`.
Returns:
JSON with "meta" (totals, scope, filters echoed back) and "results"
(each hit has `id`, `url`, `title`, `content_type`, dates, and a
`facets` map of all taxonomy arrays on the node).
| Name | Required | Description | Default |
|---|---|---|---|
| sort | No | title | |
| limit | No | ||
| scope | No | all | |
| offset | No | ||
| filters | Yes |
| Name | Required | Description |
|---|---|---|
| meta | Yes | |
| results | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description explains that invalid facet names cause a 400 error, the AND combination logic for multi-facet filters, and that each facet may appear at most once. This goes well beyond the annotations (readOnlyHint=true, destructiveHint=false) by detailing error conditions and filtering semantics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a brief summary followed by detailed param and return sections. Each sentence serves a distinct purpose with no redundancy, and the critical usage sequence (call taxonomy_facets first) is front-loaded in the opening paragraph.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the high parameter count (5), nested object filters, and 0% schema coverage, the description is complete. It explains the return format (with meta and results fields), prerequisites, error behavior, and all parameter semantics. The presence of an output schema further reduces the burden on the description, which still goes above and beyond.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description bears full burden to explain parameters. It provides detailed semantic info for all 5 parameters including example usage for filters, valid options for scope, limit range and default, offset default, and sort options. This fully compensates for the lack of schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists TCLP content filtered by taxonomy facets, and distinguishes it from siblings by referencing the need to call taxonomy_facets first to get valid facet names. This differentiates it from the similar sibling tools like search and entity_lookup.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly tells the agent to call taxonomy_facets first to learn valid facet names and slugs, warns that invalid names result in a 400 error, and explains that multi-facet filters are combined with AND. This provides clear when-to-use and when-not-to-use guidance relative to the sibling tool taxonomy_facets.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List taxonomy facets and their value slugs across TCLP content.
Facets are taxonomy categories like `sector`, `practice_area`,
`application`, and `jurisdiction`. Each facet returns the list of slugs
that actually appear on the graph, with counts. Use this to discover
the vocabulary, then call `taxonomy_content` with chosen slugs.
Args:
scope: Which labels to include — `clause` (ClauseName only),
`guide` (Guide only), or `all` (both, the default).
Returns:
JSON with "meta" and "facets". Each facet has `name`, `applies_to`
(list of Neo4j labels carrying it), and `values` (list of
`{slug, count}`, sorted by count desc).
| Name | Required | Description | Default |
|---|---|---|---|
| scope | No | all |
| Name | Required | Description |
|---|---|---|
| meta | Yes | |
| facets | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true (safe read operation) and destructiveHint=false, so the behavioral burden is reduced. The description adds value by explaining the return structure and that it returns vocabulary with counts, but does not mention rate limits or data freshness.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (purpose, scope, returns) and front-loaded with the primary purpose. However, it could be slightly more concise—the 'Returns' section provides details that might be inferred from the output schema but adds clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low parameter count (1), zero required parameters, rich annotations (readOnlyHint, openWorldHint), and available sibling tools, the description is complete. It clearly explains the tool's role in the workflow (discovery before taxonomy_content) and all relevant aspects.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate for the parameter 'scope'. It does this by detailing the three possible values (clause, guide, all) and their meanings. This provides substantial value beyond the input schema, which only gives type and default.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it lists taxonomy facets and their value slugs across TCLP content, distinguishing it from sibling tools like taxonomy_content which uses chosen slugs. The specific verb 'list' and resource 'taxonomy facets' make the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains when to use this tool: to discover the vocabulary before calling taxonomy_content. However, it does not explicitly mention when not to use it or alternatives among siblings (like entity_lookup or search).
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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The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
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Each tool has a clearly distinct purpose: entity_lookup for exact entity matching, search for semantic/full-text search, taxonomy_content for filtered browsing, and taxonomy_facets for discovering filter options. There is no overlap or ambiguity.
All tool names follow a consistent snake_case pattern with descriptive, domain-specific names (entity_lookup, search, taxonomy_content, taxonomy_facets). The naming is predictable and self-explanatory.
With 4 tools, the set is well-scoped for searching and browsing a legal content knowledge graph. Each tool serves a clear, necessary function without redundancy, and the count feels appropriate for the domain.
The tools cover key discovery use cases: exact lookup, free-text search, faceted browsing, and facet exploration. A minor gap is the lack of tools for creating, updating, or deleting content, but that aligns with a read-only knowledge graph server, so completeness is high.