Pixio-MCP

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct action (listing, param fetching, cost estimation, generation, status polling, download, upload, credit checking) with no overlap. The descriptions clearly differentiate their purposes.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern using snake_case (e.g., list_models, get_model_params, upload_media). No mixing of conventions.
Tool Count5/5
With 9 tools covering the full generation workflow (discovery, parameter retrieval, cost estimation, run, status, download, upload, credits), the count is well-scoped and each tool serves a necessary role.
Completeness4/5
The tool surface covers the essential lifecycle: list, params, estimate, generate, poll, download, upload, and credit check. A minor gap is the absence of cancel or delete operations, but these are not critical for the core use case.
Average 4.8/5 across 9 of 9 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 8 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
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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?
No annotations provided, so description carries full burden. It describes the return format verbatim, gotchas about select-type params and optional-with-default params, and error condition. It does not mention destructive actions or auth, but as a read-only fetch, that is implied.
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 overview, step context, gotchas, args, and returns. It is slightly verbose but each sentence adds value given the need to explain gotchas and contract.
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 no output schema, the description provides return structure. One param is well explained. The tool is part of a sequence, and the description explains its role fully. No missing critical information.
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?
Only one parameter (model_id) with 0% schema description coverage. The description adds meaning: 'Catalog model id from list_models, e.g. "pixio/flux-1/schnell".' This provides useful context 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 'Fetch the exact input schema for one Pixio model' and positions it as step 2 of a three-call contract (list_models -> get_model_params -> generate). This distinguishes it from siblings like list_models and generate.
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 explicitly says 'Step 2 of the three-call contract' and provides critical gotchas for building params for generate(). It does not explicitly state when not to use it, but the contract implies its specific role. It also mentions error behavior for unknown model_id.
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?
No annotations provided, so description carries full burden. Details cost determination logic, return structure, and warning conditions. Lacks some side-effect info, but as a read-only estimation tool, this is sufficient.
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?
Well-structured with paragraphs and bullet points. Front-loaded with purpose. Every sentence adds value, though slightly verbose in cost source enumeration.
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 no output schema and low schema coverage, description thoroughly explains return format, three cost sources, and warning conditions. Covers workflow context and parameter details completely.
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 0%, but description provides clear explanations: model_id with example, params as the intended generate object. 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?
Clearly states the tool estimates credit cost before spending. Positions it in a workflow between get_model_params and generate, distinguishing it from siblings like generate and list_models.
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 call after get_model_params and before generate, referencing the three-call contract. Explains cost source hierarchy (estimate > catalog > unknown) and when warning appears.
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?
With no annotations provided, the description fully covers behavioral traits: polling with backoff (2s to 10s jittered), timeout handling, credit reconciliation, and return shapes on success and failure. 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-organized into logical sections (purpose, usage, behavior, Args, Returns) with no unnecessary words. Every sentence provides 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 complexity (polling, timeout, error handling), the description covers return types, error codes, and usage patterns comprehensively. No output schema exists, but the description includes the return shape.
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's 'Args' section adds clear meaning: 'generation_id' is the ID from 'generate', and 'timeout_s' is max wait with server default. This fully compensates for missing schema descriptions.
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 'Block until a generation reaches a terminal status, or time out,' providing a specific verb and resource. It distinguishes from siblings by mentioning its use after 'generate' returns TIMEOUT_PENDING or for jobs with 'wait=false'.
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 explicitly notes when to use the tool (after TIMEOUT_PENDING or wait=false) and explains that it can be called multiple times on timeout. However, it does not explicitly contrast with alternatives like 'get_generation' for non-blocking scenarios.
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?
Despite no annotations, the description thoroughly discloses behavioral traits: spend guardrails (BUDGET_EXCEEDED), URL-only input requirement, validation errors, waiting/polling mechanisms, timeout handling, output URL expiration, and error codes. No contradictions with annotations since none exist.
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 summary, step-by-step instructions, parameter details, and return format. While it's fairly long, every sentence adds value given the tool's complexity. A slight reduction could improve conciseness, but it remains efficient.
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, nested objects, no output schema), the description is remarkably complete. It explains return values for success and failure, all possible error codes, and the behavior of wait=true/false, leaving no major 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 compensates fully with an 'Args:' section explaining each parameter's purpose, defaults, and constraints. For 'params', it provides critical context about building from get_model_params and URL-only media fields, which the schema lacks.
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 that the tool runs a media generation job on Pixio with spend guardrails. It distinguishes itself from siblings by being the final step of a 3-call discovery contract (list_models -> get_model_params -> generate), making its purpose very specific.
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 the tool (as the final step of the discovery contract) and provides alternatives for waiting behavior (e.g., using wait_for_generation if timeout occurs). It also gives detailed instructions on how to build params from get_model_params and URL requirements.
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?
Despite no annotations, description discloses that remaining_balance is best-effort and None while processing, explains error structure, and notes it's a snapshot. Fully transparent.
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?
Concise but complete. Every sentence adds value, front-loaded with purpose. Structured with use case, status info, args, returns.
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?
No output schema, but description provides detailed return fields including error structure. For a single-param tool, it's fully complete.
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?
Only one parameter, and the description adds meaning by stating that generation_id is the Id returned by generate, which is beyond the schema's minimal 'Generation Id'.
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 fetches status and outputs of a generation (snapshot). It uses specific verb 'Fetch' and resource 'generation', and distinguishes from sibling 'wait_for_generation' by stating no polling.
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 (check on job with wait=false or after timeout) and when to use alternative (wait_for_generation for blocking). Also explains status progression.
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?
No annotations provided, so description carries full burden. It discloses that output URLs may be signed and expire, behavior for error states (processing vs failed), that dest_dir is created if missing, ~ expansion, default directory, and file naming convention. Comprehensive behavioral 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?
Description is multi-paragraph but well-structured: starts with main purpose, then usage instructions, then parameter details, then return value. Every sentence provides value. No fluff.
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 (context: true), description explains return format with example and file naming convention. It also provides enough context for an agent to use correctly with siblings. Complete for this 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 describes parameters but without descriptions (coverage 0%). Description adds critical meaning: generation_id is 'id returned by generate', dest_dir is 'Target directory, created if missing (~ expanded), defaults to server's configured download directory'. Fully compensates for lack of schema descriptions.
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 downloads every output file of a succeeded generation. It specifies the verb 'download', resource 'output files', and context 'after generation succeeds', distinguishing it from siblings like generate, wait_for_generation, etc.
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 states when to use: after generate(wait=true) or wait_for_generation reports succeeded. Provides warnings about URL expiration (~1 hour) and explains error conditions (VALIDATION error if processing, failed status with details). Gives clear guidance on when not to use and what alternatives to use (wait_for_generation).
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?
With no annotations provided, the description fully discloses behavior: it returns a detailed JSON structure, explains the effect of arguments, and specifies how ledger_limit handles negative values. No contradictions present.
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-organized with a summary, usage context, args, and returns sections. Every sentence adds value without redundancy, achieving efficiency and clarity.
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 two optional parameters and no output schema, the description covers all necessary aspects: purpose, usage, parameters, and return format. It is fully 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?
Schema has 0% description coverage, but the description thoroughly explains both parameters: include_ledger_tail triggers ledger entries, ledger_limit controls count with default 10 and negative treated as 0.
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 'Report the Pixio account's current credit balance' and distinguishes the tool from siblings like 'estimate_cost' and mentions that other generation tools include 'remaining_balance', making its unique purpose evident.
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 provides when to use (check affordability before a job, audit spend after) and when not to use (since generation results already include remaining_balance), with clear alternatives.
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 caching behavior (~10 minutes), truncation of descriptions to 200 characters, clamping on limit parameter, and handling of negative offset. Provides detailed return structure without contradicting any annotations (none provided).
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?
Well-structured: summary sentence, contract overview, bulleted Args, Returns section. Every sentence adds value without unnecessary verbosity. Ideal length for an AI agent.
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?
Comprehensive for a tool with 4 parameters, no output schema, and no annotations. Covers parameter constraints, return format, caching, and positions within the tool ecosystem (sibling tools). No 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?
Despite 0% schema description coverage, the description thoroughly explains each parameter: type examples, query as case-insensitive substring matching with examples, limit clamped to 1-200 with default 50, offset with negative handling. 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 clearly states the tool browses the Pixio model catalog with optional filters. It identifies the resource and action, and distinguishes itself from siblings by being step 1 of a three-call contract.
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 describes the three-call contract for running generations, positioning list_models as the first step to find a model ID. Provides clear when-to-use guidance with alternatives (get_model_params and generate) listed.
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?
No annotations provided, so description fully handles transparency. Discloses behavior: source can be URL (mirrored) or local file (uploaded with ~ expansion), directories rejected, returns a detailed dict. Notes that size_bytes is None for remote URLs.
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?
Well-structured: brief intro, usage context, then clearly formatted Args and Returns sections. Every sentence is informative and earns its place.
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?
With one parameter and an output schema present, the description fully covers what the tool does, when to use it, input details, and output structure. No 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 coverage is 0%, so description must compensate. It thoroughly explains the 'source' parameter: accepts http(s) URL or local file path, with ~ expansion, and that directories are rejected. Adds significant value beyond the schema's minimal type info.
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: uploading a local file or mirroring a remote URL to Pixio for a permanent public media URL. It distinguishes itself by explaining why it's necessary before the 'generate' tool, which only accepts http(s) URLs.
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 states to use this tool before 'generate' when media parameters like image_url, video_url, audio_url are needed. Provides clear context on what it returns and how to pass the result to generate.
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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