A
licenseA
qualityF
maintenanceMCP server for FOLIO, the Federated Open Legal Information Ontology. Makes the full ontology available to AI agents as searchable, browsable tools.
12
5
MIT
tclp-mcp
Server Details
MCP server for The Chancery Lane Project's climate-aligned contract clause knowledge graph.
If you are the author of this connector, you can claim ownership with GitHub, an HTTP challenge, or a DNS record. Claimed connector authors can inspect health checks, view analytics, and manage their listing.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- thechancerylaneproject/tclp-mcp
- GitHub Stars
- 0
Available Tools
4 toolsentity_lookupARead-onlyInspect
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.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | ||
| limit | No | ||
| include_full_text | No |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| meta | Yes | |
| results | Yes |
TDQS
A4.4/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description aligns with annotations (readOnlyHint=true, destructiveHint=false) by describing a lookup operation. It adds context about deterministic name matching (case-insensitive, exact match) and warns that `include_full_text` bodies are large, which goes beyond annotations. However, it doesn't mention any potential performance implications of large result sets or AI model context limitations, keeping it from a 5.
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 extremely concise and well-structured: a one-line purpose statement, a clear paragraph distinguishing from `search` with usage advice, a brief 'Args' section documenting parameters, and a concise 'Returns' summary. Every sentence adds value with no redundancy.
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 tool has 3 simple parameters (one required), annotations declaring it safe and open-world, and an output schema providing return structure, the description is complete. It covers the purpose, usage, parameter details, and return format comprehensively. No gaps are evident for this level of complexity.
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 description coverage is 0%, so the description fully carries parameter documentation. It explains `name` is an 'exact match, case-insensitive' search term, `limit` is a 'Maximum number of results' (1–50), and `include_full_text` governs including full body text (Markdown) with usage warnings. This adds significant value beyond the schema's bare types and defaults, but a 5 would require even more detail (e.g., default value re-stated for `name`).
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 tool finds 'TCLP content nodes (clauses, glossary terms) associated with a named concept', using specific verbs ('Find') and resources ('Entity nodes in the knowledge graph'). It distinguishes itself from the sibling 'search' by highlighting deterministic vs. semantic matching, though it doesn't explicitly contrast with 'taxonomy_content' or 'taxonomy_facets'.
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?
The description explicitly contrasts with 'search' ('Unlike `search`...'), explains when to use this tool ('when you have a specific term... and want to retrieve every clause or glossary entry that explicitly references it'), and provides detailed guidance for the `include_full_text` parameter ('request only when you need the content, and prefer a small `limit` when you do'). This is exemplary usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
searchARead-onlyInspect
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).
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | ||
| rerank | No | ||
| node_type | No | all | |
| include_full_text | No |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| meta | Yes | |
| results | Yes |
TDQS
A4.8/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds significant behavioral context beyond the annotations (readOnlyHint, openWorldHint, destructiveHint): it explains the fusion search approach, RRF reranking toggle, full-text body size considerations, and the return structure (meta and results with scores, relationships, full_text). The annotations already indicate a safe, read-only operation, and the description aligns with and enriches that understanding.
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 and well-structured, using an Args section with bullet-point-like formatting and a Returns section. Every sentence provides necessary information without redundancy. The key purpose is front-loaded in the opening sentence.
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 tool's complexity (5 parameters, 1 required, fusion search with reranking, large result bodies), the description covers all important aspects: parameter semantics, usage trade-offs, output format, and performance considerations. The presence of an output schema further reduces the need to describe return fields, and the description still provides enough context for an agent to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by explaining each parameter's semantic meaning. It clarifies query as free-text (max 1000 chars), node_type with enumerated values ('tclp', 'lrsf', 'all'), limit as a numeric range (1–50), rerank as a toggle for RRF combining graph and text, and include_full_text with a clear trade-off explanation. This adds essential meaning beyond the schema's basic types and defaults.
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 with a specific verb ('Search'), a defined resource ('TCLP knowledge graph'), and the method ('fusion search (semantic + BM25)'). It distinguishes itself from sibling tools like entity_lookup and taxonomy_facets by focusing on full-text semantic search across multiple content types.
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 parameter usage guidance (e.g., when to enable include_full_text, acceptable limit range, node_type options) but does not explicitly state when to prefer this tool over sibling tools or when not to use it. The context is clear enough from the name and description that an agent can infer appropriate usage, but explicit exclusions are missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
taxonomy_contentARead-onlyInspect
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).
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| sort | No | title | |
| limit | No | ||
| scope | No | all | |
| offset | No | ||
| filters | Yes |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| meta | Yes | |
| results | Yes |
TDQS
A4.9/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as read-only (readOnlyHint true) and non-destructive (destructiveHint false). The description reinforces this with a listing action and adds key behavioral details: multi-facet AND logic, 400 error for invalid facets, and the constraint that each facet appears at most once. No contradiction with annotations.
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 well-structured with a brief summary in the first sentence, followed by usage guidance and a clear parameter listing. It is relatively concise but could be slightly trimmed; however, the completeness justifies the length.
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 tool has 5 parameters (1 required), no enums, nested object input, and an output schema, the description fully covers parameter semantics, filtering behavior, error cases, and return format. The output schema is present, so the description of return values is sufficient (meta and results with key fields). No gaps remain.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description must fully compensate, and it does. It explains `filters` as a mapping of facet name to value slug, gives an example, and notes the 'at most once' constraint. It defines valid values for `scope` (clause/guide/all) and `sort` (title, date_published_desc, date_modified_desc). It also provides default values and ranges for `limit` (1-100) and `offset`.
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 lists TCLP content filtered by taxonomy facet=value. It specifies the resource (TCLP content like clauses and guides), the action (list), and the filtering mechanism. This distinguishes it from siblings like `taxonomy_facets` (which returns available facets) and `search` (general search).
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?
The description explicitly instructs to call `taxonomy_facets` first to learn valid facet names, and warns that using an invalid name returns a 400 error. It also explains the AND combination of multi-facet filters. This provides clear when-to-use and prerequisite guidance, differentiating from sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
taxonomy_facetsARead-onlyInspect
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).
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| scope | No | all |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| meta | Yes | |
| facets | Yes |
TDQS
A4.9/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, destructiveHint=false, and openWorldHint=true. The description adds significant behavioral context: facets return only slugs that actually appear on the graph, include counts, and are sorted by count descending. It also explains the scope parameter's effect on which labels are included. This goes well beyond the annotations, providing essential behavior details for an agent.
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 well-structured: it starts with the main purpose, then explains facets, gives usage guidance, and finally details the parameter and return value. It is informative but not overly verbose. Minor improvement could be made by trimming the explanation of facets (e.g., 'Facets are taxonomy categories...') to be more concise. Still, it is effective and 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?
Given the single optional parameter, detailed annotations, and the presence of an output schema (though not provided), the description is remarkably complete. It explains the parameter constraints, the return structure (JSON with 'meta' and 'facets', each facet including name, applies_to, and values), and the sorting. The only minor omission is the content of 'meta', but it is not critical for the agent's use. The description fully equips the agent to invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has a single optional parameter 'scope' with no description or enum constraints (schema description coverage 0%). The description fully compensates by enumerating the valid values: 'clause', 'guide', 'all', explaining what each means, and noting the default. This adds crucial meaning that the schema alone fails to provide, enabling correct invocation.
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: 'List taxonomy facets and their value slugs across TCLP content.' It provides concrete examples of facets (sector, practice_area, etc.) and mentions counts. It also distinguishes from the sibling tool taxonomy_content by advising to use this first and then call taxonomy_content with chosen slugs. The verb 'list' and resource 'taxonomy facets and value slugs' are specific and unambiguous.
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?
The description explicitly explains when to use this tool: 'Use this to discover the vocabulary, then call taxonomy_content with chosen slugs.' This directly tells the agent to use taxonomy_facets first to obtain valid slugs before using taxonomy_content. It does not mention entity_lookup or search, but the guidance is clear and sufficient for selecting the correct tool in a workflow.
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
A4.7/5.0
Disambiguation5/5
Each tool has a clearly distinct purpose: entity_lookup for deterministic name matching, search for semantic/BM25 fusion, taxonomy_content for faceted filtering, and taxonomy_facets for exploring available facets. There is no ambiguity or overlap between them.
Naming Consistency5/5
All tool names follow a consistent snake_case pattern with descriptive verb_noun pairs (e.g., entity_lookup, taxonomy_content). Even 'search' fits as a verb-based name. No mixing of conventions.
Tool Count5/5
With only 4 tools, the server is tightly scoped to querying a knowledge graph. Each tool earns its place, covering entity lookup, free-text search, and taxonomy browsing. Neither too few nor too many.
Completeness4/5
The tool set covers the main query paradigms for this knowledge domain. However, a direct fetch-by-ID tool is missing (users must use search or entity_lookup as workarounds), and taxonomy filtering is limited to TCLP content, not LRSF. Minor gaps exist but agents can still accomplish core tasks.