liquid-mcp

Server Quality Checklist

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Latest release: v0.1.2
Disambiguation5/5
Each tool has a distinct role: setup (connect), preview (discover), estimate, fetch data, list adapters, and query. No overlap in functionality, and descriptions clearly differentiate them.
Naming Consistency5/5
All tools follow the consistent pattern 'liquid_verb' or 'liquid_verb_noun' in snake_case, e.g., liquid_connect, liquid_list_adapters. No mixing of styles.
Tool Count5/5
6 tools is well-scoped for the server's purpose of API integration: discovery, setup, estimation, fetching, querying, and listing. Not too few or too many.
Completeness4/5
Covers most lifecycle steps for read-only API usage: discover, connect, estimate, fetch, and query. Missing maybe a tool to delete/disconnect an adapter, but that is a minor gap given the read-only focus.
Average 4.8/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 1 of 2 community issues answered or closed in the last 6 months
- 100 commits in the last 12 weeks
- Last stable release on June 4, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
This repository is licensed under AGPL 3.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. Description adds context: 'no HTTP call or LLM call', 'read-only and free', and describes return structure with confidence/source, which goes beyond annotations.
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?
Three concise sentences, front-loaded with purpose and output, followed by usage guidance. No redundant information; every sentence adds value.
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 simple pre-flight nature, output schema is referenced, schema covers parameters, annotations cover safety, and description provides complete usage context including prerequisites and alternative tools.
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 100% so baseline is 3. Description mentions adapter_id is from liquid_connect and endpoint optional defaults to primary, which reinforces schema but doesn't add new semantic meaning beyond it.
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?
Description explicitly states 'Pre-flight estimate for a fetch' with specific outputs (item count, bytes, tokens, credits, latency) and contrasts with siblings liquid_fetch and liquid_query, making the tool's role 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?
Clearly instructs to check this before a large liquid_fetch and suggests using liquid_query to narrow the pull if needed. Also notes prerequisite of an adapter_id from liquid_connect, providing explicit when-to-use and alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds that it is 'deterministic, no LLM call' and mentions a read-only outbound HTTP request with rate-limiting, providing helpful behavioral context beyond the structured annotations. No contradictions.
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?
Description is fairly concise and well-structured, leading with key characteristics (deterministic, read-only) and following with mode details and comparison. While slightly long, every sentence contributes value; 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 presence of output schema (not shown but indicated), annotations, and 7 parameters with full schema coverage, the description sufficiently covers purpose, usage modes, side effects, return types, and sibling differentiation. It is complete for a query tool with this 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 coverage is 100% with descriptions for each parameter. The description adds context by explaining the two modes and which parameters belong to each (e.g., group_by/agg for aggregate, where/fields/limit for search), helping the agent understand parameter relationships beyond the 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?
Description clearly states the tool performs server-side search or aggregation through an adapter, is deterministic and read-only. It distinguishes two modes (search with where/fields/limit, aggregate with group_by/agg) and explicitly contrasts with sibling tool liquid_fetch, making its purpose unambiguous and well-differentiated.
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?
Provides explicit guidance on when to use this tool over liquid_fetch ('whenever you only need a filtered slice, a count, or a summary'), lists side effects (rate-limited like liquid_fetch), and describes the two operational modes. This equips the agent with clear decision-making criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses behavioral traits beyond the annotations: it mentions side effects ('makes outbound HTTP(S) requests to url, calls the configured LLM, and persists the adapter + any credentials under ~/.liquid') and confirms idempotency in a user-friendly way ('reuses the existing adapter instead of duplicating it'). This meets the high bar for transparency even with good annotations, as it adds context about persistence and auth requirements.
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 and front-loaded with the main purpose. It contains several sentences, each adding value (e.g., side effects, idempotency, alternatives). While slightly longer than minimal, every sentence is justified and no redundancy is present. Could be slightly tighter, but overall effective.
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 (3 parameters, side effects, idempotency, output schema with adapter_id), the description covers all necessary context: usage, behavior, parameter details, return value, and alternatives. It also notes credentials storage. The description is complete enough for an AI agent to understand when and how to use the tool.
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?
Despite 100% schema coverage, the description adds significant meaning: for url, it explains it accepts GraphQL, WSDL, gRPC, or WebSocket targets (not in schema). For target_model, it clarifies it must be a flat map with examples. For credentials, it states they are stored encrypted under ~/.liquid and applied automatically. This enriches the parameter understanding beyond the 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 clearly states the tool's purpose: 'One-time setup for an API.' It specifies the actions: discovers the API at a URL, maps responses to a target model, and saves a reusable adapter, returning an adapter_id. It distinguishes from siblings by explicitly naming liquid_discover (quick look without saving) and liquid_fetch (reading from an already-connected API), making the purpose unique.
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 once per API.' It tells when to use an alternative: 'For a quick look without saving anything, use liquid_discover instead; to read data from an already-connected API, use liquid_fetch.' This clearly delineates when to use this tool versus its siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses side effects: makes outbound HTTP(S) requests and may call LLM for REST heuristic. States read-only and nothing persisted. No contradiction with annotations (readOnlyHint, destructiveHint, idempotentHint).
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?
Description is about 5 well-structured sentences, no fluff, front-loaded with key action. Every sentence adds value.
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 output schema exists, description adequately covers the return shape (service name, discovery method, auth type, endpoint list). No gaps.
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 coverage is 100%, description adds meaning by explaining credentials are optional, stored encrypted, and implies automatic usage on later fetches. Adds value beyond 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 clearly states it inspects an API's shape (service name, discovery method, auth type, endpoint list) without saving, and distinguishes from liquid_connect which also saves an adapter.
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?
Explicitly says to use for previewing unknown APIs, and to call liquid_connect when ready to read data. Provides clear when-to-use and when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnly, destructive, idempotent hints. Description adds 'read-only, no network call, no LLM' and specifies data source (~/.liquid), providing context beyond annotations. 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, each earning its place. First states function, second provides usage context. No wasted words, well-structured.
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 zero parameters, rich annotations, and presence of output schema, the description fully covers what the tool does, its data source, fields in output, and when to use it. Complete for the task.
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?
No parameters; schema coverage is 100%. Baseline is 4 per the rule. The description does not need to add param info, and it is clear that no parameters exist.
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 'List the adapters already connected' with specific verb and resource. It distinguishes from siblings by noting it's read-only, no network, no LLM, and explicitly names sibling tools for context.
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?
Provides explicit usage guidance: 'Call this to find an adapter_id for liquid_fetch / liquid_query / liquid_estimate, or to check whether an API is already connected before calling liquid_connect.' This directly tells when to use and implies when not to.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. Description adds 'deterministic, no LLM call', 'read-only outbound HTTP(S) request', 'subject to rate limits with proactive throttling and 429 retry hints', and return structure.
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?
Three well-organized sentences covering purpose, side effects, alternatives, and parameters. No filler.
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 tool with 2 params, output schema, and rich annotations, the description provides all necessary context: purpose, prerequisites, behavior, return format, and sibling differentiation.
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 covers both parameters fully. Description adds context by linking adapter_id to liquid_connect and end point to liquid_connect/list_adapters, and notes optional default behavior.
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?
Description clearly states verb 'Fetch records through a connected adapter', specifies determinism and no LLM call, and distinguishes from siblings by naming liquid_query and liquid_estimate as alternatives.
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?
Explicitly says when to use this tool ('pull whole records') and when to use alternatives ('liquid_query for filtering/aggregation', 'liquid_estimate for sizing'). Also notes prerequisite 'adapter_id from liquid_connect'.
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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