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.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- thechancerylaneproject/tclp-mcp
- GitHub Stars
- 0
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
MCP client
Glama
MCP server
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
100% free. Your data is private.
Tool Definition Quality
A4.4/5.0
Tool DescriptionsA
Average 4.7/5 across 4 of 4 tools scored.
Server CoherenceA
Disambiguation5/5
Each tool has a unique, well-defined role: entity_lookup is deterministic exact-match, search is semantic/BM25, taxonomy_content is faceted filtering, and taxonomy_facets is vocabulary discovery. No two tools overlap in purpose, and the descriptions explicitly contrast entity_lookup with search.
Naming Consistency2/5
Tool names are not consistent in grammatical pattern: entity_lookup is noun-verb (reverse of verb-noun), search is a bare verb, taxonomy_content and taxonomy_facets are noun-noun compounds. There is no predictable convention, though each name is individually descriptive.
Tool Count5/5
With only 4 tools, the set is tightly scoped for a read-only knowledge graph retrieval server. Each tool covers a distinct access mode (search, exact lookup, taxonomy browsing), so none feel redundant or missing at a high level.
Completeness4/5
The server covers the primary retrieval workflows: full-text/semantic search, deterministic entity lookup, and taxonomy-based browsing with facet discovery. The only notable gap is a direct get-by-ID tool, but agents can obtain full text via include_full_text on existing tools, so this is a minor workaround.
Available Tools
4 toolsentity_lookupAInspect
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 |
Tool Definition Quality
A4.7/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full transparency burden. It discloses key behavioral traits: the match is 'deterministic,' 'exact match, case-insensitive,' and `include_full_text` is described as large and off by default. The return structure is also outlined ('meta' and 'results'). It does not explicitly state read-only behavior or discuss errors/timeouts, but for a lookup tool this is a strong disclosure.
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 well-structured with a purpose sentence, an explicit comparison to `search`, an Args section, and a Returns section. Every sentence serves a functional purpose, and the most critical information (what it does, when to use it) is front-loaded without being verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with three parameters, no annotations, and no output schema, the description covers the core aspects: lookup semantics, parameter constraints, performance guidance for large text, and a summary of return values. It falls slightly short by not detailing the shape of the `meta` field or error handling when no match is found, but it is otherwise quite complete.
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 zero property descriptions (0% coverage), so the description must compensate. It does so excellently: `name` is 'exact match, case-insensitive,' `limit` is bounded 1–50, and `include_full_text` is explained with a performance warning about large bodies and a recommendation for a small `limit`. This adds significant semantic value beyond the bare schema.
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 opens with a clear, specific statement: 'Find TCLP content nodes (clauses, glossary terms) associated with a named concept.' It further distinguishes itself from the sibling `search` by contrasting 'deterministic name match' with 'relevance-ranked semantic search,' making its resource and verb 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 states when to use this tool: 'Use it when you have a specific term or concept... and want to retrieve every clause or glossary entry that explicitly references it.' It also provides a clear contrast to `search`, implying when not to use it (for semantic/relevance-ranked queries), fulfilling the alternatives requirement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
searchAInspect
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 |
Tool Definition Quality
A4.4/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description discloses several behavioral traits: fusion search behavior, RRF reranking, the large size of full-text bodies, and the structure of the response. It does not mention rate limits or auth, but for a read-only search tool this is acceptable and above the minimum.
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 tightly organized: a one-sentence overview, then an Args list and a Returns block. Every sentence provides necessary information, with no fluff or repetition. It is front-loaded with the core purpose.
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 5 parameters, 0% schema coverage, and no output schema, the description fully compensates: it explains every parameter and explicitly describes the return JSON structure (meta, results, ranked hits with fields). It even cautions about the impact of include_full_text. This is complete for the tool's complexity.
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?
Schema description coverage is 0%, so the description carries full responsibility for parameter explanations. It does so thoroughly: query (max 1000 characters), node_type (with three scopes), limit (1–50), rerank (RRF), and include_full_text (off by default, bodies large, prefer small limit). This adds significant meaning beyond the bare schema.
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 states a specific verb ('Search') and resource ('the TCLP knowledge graph') with a concrete method ('fusion search (semantic + BM25)'). This clearly distinguishes it from siblings such as entity_lookup or taxonomy_content, which are lookup and content retrieval tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear parameter-level usage guidance (e.g., when to enable include_full_text, how to scope node_type), but it does not explicitly compare this tool to its siblings or state when to prefer search over entity_lookup. Usage is implied by the tool's purpose, but no alternative references are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
taxonomy_contentAInspect
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 |
Tool Definition Quality
A4.8/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully carries the burden. It discloses AND semantics for multi-facet filters, the 'at most once' constraint, error behavior (400), default values for limit/offset, allowed sort values, and the exact return structure. This goes well beyond basic expectations.
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 well-structured with an opening summary, an Args list, and a Returns section. Every sentence adds value—no filler. Length is appropriate for five parameters and return details.
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 there is no output schema, the description fully specifies the response shape. It also covers error handling, pagination defaults, sorting, and filtering semantics. A new agent would know exactly how to call this tool and what to expect.
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?
Schema description coverage is 0%, so the description must explain all parameters. It does: filters with example mapping and constraints, scope options, limit range, offset paging, and sort enum. No gaps remain.
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 opens with a specific verb and resource: 'List TCLP content (clauses, guides) filtered by taxonomy facet=value.' This clearly distinguishes it from sibling tools like taxonomy_facets (which lists facets) and search (full-text).
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?
Explicitly instructs to call taxonomy_facets first to learn valid facet names/slugs and warns that invalid names return 400. This gives strong usage context and a prerequisite, but does not explicitly contrast with alternatives like search or entity_lookup, so a 4 rather than 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
taxonomy_facetsAInspect
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 |
Tool Definition Quality
A5/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden and does so thoroughly. It details the return structure (JSON with meta and facets), facet fields (name, applies_to, values), and behavior (values are sorted by count desc, slugs that actually appear on the graph).
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 well-structured with clear sections for Args and Returns, uses precise technical language, and contains no filler. Every sentence contributes useful information.
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 simple one-parameter tool without an output schema, the description is remarkably complete. It explains the purpose, usage, scope effects, return format, and even the relationship to the sibling tool taxonomy_content, leaving no important gaps.
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?
Schema description coverage is 0%, but the description fully documents the only parameter, scope, including the exact allowed values ('clause', 'guide', 'all') and their meanings. This compensates completely for the schema's lack of detail.
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 starts with a specific verb and resource: 'List taxonomy facets and their value slugs across TCLP content.' It clearly states what the tool does and identifies the facet categories, distinguishing it from sibling tools like taxonomy_content.
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 provides explicit usage guidance: 'Use this to discover the vocabulary, then call taxonomy_content with chosen slugs.' This tells the agent when to use the tool and points to the appropriate alternative for subsequent actions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!
Related MCP Servers
- AlicenseCqualityDmaintenanceA Model Context Protocol (MCP) server that connects GraphDB's SPARQL endpoints and Ollama models to Claude, enabling Claude to query and manipulate ontology data while leveraging various AI models.283MIT
- Flicense-qualityBmaintenanceMCP server for building knowledge graphs from documents. It ingests PDF/PPTX/DOCX files, transcribes to Markdown, and uses LLM to bootstrap entity patterns and build a JSONL knowledge graph with RAG chunks.
- Flicense-qualityCmaintenanceMCP server for first-pass legal contract review. It loads txt/docx/pdf contracts, detects standard clauses, flags heuristic risks, summarizes, and compares agreements.