pydantic-mcp is an MCP server for inspecting, validating, and working with Pydantic models and Python type contracts in LLM workflows, providing deterministic validation, serialization, schema generation, and migration assistance.
Discover Models: Scan configured packages to find exported Pydantic BaseModel classes, with optional filtering and package selection.
Inspect & Explain Types: Resolve any Python type annotation or model into a structured description, and generate human-readable documentation covering fields, defaults, aliases, constraints, and nested structures.
Validate Data: Validate arbitrary payloads against a model or Python type expression, with support for strict mode, Python/JSON mode, and validation context.
Serialize Data: Dump validated data using Pydantic serialization, with options for aliases, excluding unset/none/default values, round-trip mode, and Python/JSON output.
Generate JSON Schema: Produce validation or serialization JSON Schema for any model or type, with optional $defs definitions.
Create Example Payloads: Generate valid (and optionally invalid) example payloads for a target model or type.
Compare Validation Modes: Contrast model vs. TypeAdapter, strict vs. non-strict, and Python vs. JSON validation behavior for the same data.
Migrate v1 to v2: Analyze code snippets or model sources for common Pydantic v1-to-v2 migration issues, with optional auto-fix suggestions.
Parse Partial JSON: Best-effort parse incomplete JSON strings and validate the resulting fragment against a target type.
Generate Model from JSON: Infer and generate candidate Pydantic model definitions from a sample JSON payload.
MCP Integration: Expose tools, resources, and prompts via the MCP protocol, with HTTP health and readiness endpoints for server monitoring.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@pydantic-mcp Generate a JSON schema for the UserProfile model"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
pydantic-mcp is an MCP server for inspecting Pydantic models and Python type contracts. It is built for LLM workflows that need deterministic validation, serialization, schema generation, model explanations, and migration help.
Discover Pydantic BaseModel classes across configured packages.
Resolve targets from import paths, short model names, Python type expressions, or inline model snippets.
Validate arbitrary payloads with TypeAdapter or model behavior.
Serialize validated data in Python or JSON mode.
Generate validation and serialization JSON Schema.
Explain fields, defaults, aliases, decorators, constraints, and nested models.
Generate valid and invalid example payloads.
Infer candidate Pydantic models from sample JSON payloads.
Compare strict/non-strict and Python-vs-JSON validation behavior.
Analyze common Pydantic v1 to v2 migration issues.
Parse partial JSON with pydantic_core.from_json.
Expose MCP tools, resources, prompts, plus HTTP health/readiness routes.
Related MCP server: openapi-mcp-proxy
list_models
inspect_type
explain_model
validate_data
serialize_data
generate_json_schema
create_example_payload
generate_model_from_json
compare_validation_modes
migrate_v1_to_v2
parse_partial_json
pydantic://server/capabilities
pydantic://project/settings
pydantic://project/import-roots
pydantic://project/errors/recent
pydantic://project/models/changed
pydantic://models/index
pydantic://models/{qualified_name}
pydantic://schemas/{qualified_name}?mode=validation|serialization
pydantic://examples/{qualified_name}
pydantic://migration/rules
pydantic://reference/overview
explain model
generate api contract docs
debug validation error
design a model from example json
review schema compatibility
migrate to pydantic v2
Install dependencies:
uv syncRun over stdio:
uv run python mcp_server.py --transport stdioRun over HTTP:
uv run python mcp_server.py --transport http --host 127.0.0.1 --port 8000Health endpoints:
GET /healthz
GET /readyz
Important environment variables:
PYDANTIC_MCP_ALLOWED_IMPORT_ROOTS
PYDANTIC_MCP_DEFAULT_SCAN_PACKAGES
PYDANTIC_MCP_IMPORT_TIMEOUT_SECONDS
PYDANTIC_MCP_ERROR_HISTORY_LIMIT
PYDANTIC_MCP_TRANSPORT
PYDANTIC_MCP_HOST
PYDANTIC_MCP_PORT
Example:
PYDANTIC_MCP_ALLOWED_IMPORT_ROOTS=your_app.models \
PYDANTIC_MCP_DEFAULT_SCAN_PACKAGES=your_app.models \
uv run python mcp_server.py --transport stdioThese values must point at importable application packages in the runtime environment. For local smoke tests, the repository ships an installable sample package at pydantic_mcp_sample_app, but production deployments should point at your own application modules.
just testCompare model, TypeAdapter, strict, and JSON-vs-Python validation behavior.
| Name | Required | Description | Default |
|---|---|---|---|
| target | Yes | ||
| data | Yes |
| Name | Required | Description |
|---|---|---|
| result | No | |
| artifacts | No | |
| diagnostics | No | |
| resolved_target | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions comparing validation behaviors but doesn't specify what the tool does (e.g., runs comparisons, outputs differences, requires specific inputs) or any behavioral traits like side effects, permissions, or rate limits. This is inadequate for a tool with no annotation coverage.
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 concise and to the point, using a single sentence without unnecessary words. However, it's under-specified rather than efficiently structured, as it lacks detail needed for clarity, but it's not verbose or poorly organized.
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 has an output schema, the description doesn't need to explain return values, but with no annotations, 0% schema coverage, and two required parameters, it's incomplete. It hints at comparison but fails to provide enough context for effective use, making it minimally adequate but with significant gaps.
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 for undocumented parameters. It doesn't explain what 'target' or 'data' mean in the context of validation comparison, leaving their semantics unclear. The description adds no parameter-specific information beyond the vague purpose.
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 states the tool compares validation behaviors, which gives a general purpose, but it's vague about what specific resource or action is involved. It mentions 'model, TypeAdapter, strict, and JSON-vs-Python validation behavior' without specifying if this is for a particular framework or context, making it somewhat unclear but not tautological.
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?
No guidance is provided on when to use this tool versus alternatives. Given sibling tools like 'explain_model', 'validate_data', and 'inspect_type', there's no indication of how this comparison differs or when it's appropriate, leaving the agent without usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate example valid and invalid payloads for a target model or type.
| Name | Required | Description | Default |
|---|---|---|---|
| target | Yes | ||
| count | No | ||
| invalid_examples | No |
| Name | Required | Description |
|---|---|---|
| result | No | |
| artifacts | No | |
| diagnostics | No | |
| resolved_target | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions generating 'valid and invalid payloads' but doesn't specify how these are generated (e.g., random, based on schema), what formats they're in, or any limitations (e.g., rate limits, authentication needs). For a tool with no annotation coverage, this is a significant gap in transparency about its operation and constraints.
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 a single, efficient sentence that directly states the tool's function without unnecessary words. It's front-loaded with the core action ('Generate example payloads') and specifies key details ('valid and invalid', 'for a target model or type'). Every part of the sentence contributes essential information, making it highly concise and well-structured.
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 moderate complexity (3 parameters, no annotations, but with an output schema), the description is somewhat complete but has gaps. The output schema likely covers return values, reducing the need for description there. However, without annotations, it lacks details on behavioral traits, and with low schema coverage, parameter semantics are under-explained. It's adequate as a high-level overview but insufficient for full operational understanding.
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?
The description adds minimal meaning beyond the input schema. It implies 'target' refers to a 'model or type' and that payloads can be 'valid and invalid', but with 0% schema description coverage, it doesn't explain what 'target' expects (e.g., model name, type identifier), how 'count' affects output, or what 'invalid_examples' entails. The baseline is 3 due to the schema's properties being self-explanatory to some extent, but the description doesn't compensate for the low coverage.
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's purpose: 'Generate example valid and invalid payloads for a target model or type.' It specifies the verb ('generate') and resource ('example payloads'), and distinguishes the type of payloads (valid and invalid). However, it doesn't explicitly differentiate from sibling tools like 'validate_data' or 'generate_json_schema', which keeps it from a perfect score.
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 no guidance on when to use this tool versus alternatives. With siblings like 'validate_data', 'generate_json_schema', and 'list_models', there's no indication of scenarios where generating example payloads is preferred over other validation or schema-related tools. This lack of context leaves the agent to guess based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Turn a model or type into a human-readable contract.
| Name | Required | Description | Default |
|---|---|---|---|
| target | Yes | ||
| include_examples | No | ||
| include_constraints | No | ||
| include_defaults | No |
| Name | Required | Description |
|---|---|---|
| result | No | |
| artifacts | No | |
| diagnostics | No | |
| resolved_target | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the output is 'human-readable' but doesn't specify format (e.g., text, markdown, JSON), depth of explanation, or any limitations (e.g., complexity, error handling). For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.
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 a single, efficient sentence that front-loads the core purpose. There's no wasted wording or unnecessary elaboration, making it easy to parse quickly while conveying the essential function.
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 has an output schema, the description doesn't need to detail return values. However, with 4 parameters (1 required), 0% schema coverage, and no annotations, the description is too sparse. It should explain parameter roles and behavioral traits to compensate for the lack of structured metadata, especially for a tool that likely produces complex output.
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 for undocumented parameters. It only mentions 'model or type' for the 'target' parameter, ignoring the three boolean parameters ('include_examples', 'include_constraints', 'include_defaults') that control output content. The description adds minimal value beyond what the schema names imply.
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's purpose: 'Turn a model or type into a human-readable contract.' It specifies the action ('turn into'), the input ('model or type'), and the output format ('human-readable contract'). However, it doesn't explicitly differentiate from sibling tools like 'inspect_type' or 'list_models', which might have overlapping functionality.
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 no guidance on when to use this tool versus alternatives. With siblings like 'inspect_type', 'list_models', and 'generate_json_schema', there's no indication of which tool is appropriate for different scenarios. The description lacks context about prerequisites or typical use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate JSON Schema for a model or type.
| Name | Required | Description | Default |
|---|---|---|---|
| target | Yes | ||
| schema_mode | No | validation | |
| include_definitions | No |
| Name | Required | Description |
|---|---|---|
| result | No | |
| artifacts | No | |
| diagnostics | No | |
| resolved_target | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does but lacks details on permissions, rate limits, output format (though output schema exists), or error handling. For a tool with no annotations, this is a significant gap in transparency.
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 a single, efficient sentence with no wasted words. It is appropriately sized and front-loaded, clearly stating the core functionality without unnecessary elaboration.
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 has an output schema, the description does not need to explain return values. However, with no annotations, 3 parameters (0% schema coverage), and no usage guidelines, the description is minimally adequate but leaves clear gaps in understanding how to use the tool effectively.
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 schema provides no parameter descriptions. The tool description does not explain what 'target', 'schema_mode', or 'include_definitions' mean, their expected values, or how they affect the generation. It fails to compensate for the lack of schema documentation.
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's purpose: 'Generate JSON Schema for a model or type.' It specifies the action (generate) and resource (JSON Schema), though it doesn't explicitly differentiate from siblings like 'inspect_type' or 'explain_model' that might also relate to models/types. The purpose is clear but lacks sibling distinction.
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?
No guidance is provided on when to use this tool versus alternatives. The description does not mention prerequisites, context, or exclusions, such as how it differs from 'validate_data' or 'create_example_payload' in the sibling list. Usage is implied only by the purpose statement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Infer candidate Pydantic models from a JSON string or JSON-like payload.
| Name | Required | Description | Default |
|---|---|---|---|
| json_input | Yes | ||
| model_name | No | GeneratedModel |
| Name | Required | Description |
|---|---|---|
| result | No | |
| artifacts | No | |
| diagnostics | No | |
| resolved_target | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool infers models but doesn't explain what 'candidate' implies (e.g., multiple models, best guess), how it handles invalid JSON, or what the output looks like (though an output schema exists). For a tool with no annotations, this leaves significant gaps in understanding its behavior and limitations.
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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded and wastes no space, making it easy to parse quickly. This is an example of optimal conciseness for a simple tool.
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 has an output schema, the description doesn't need to explain return values. However, with no annotations, 0% schema coverage, and two parameters, the description is minimal. It covers the basic purpose but lacks details on usage, parameters, and behavioral traits. For a model-generation tool, this is a bare-bones description that leaves the agent with incomplete context.
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%, meaning parameters are undocumented in the schema. The description mentions 'JSON string or JSON-like payload' which hints at the 'json_input' parameter but doesn't clarify its format or constraints. It doesn't address the 'model_name' parameter at all. With low coverage, the description fails to compensate adequately, leaving parameters poorly explained.
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's purpose: 'Infer candidate Pydantic models from a JSON string or JSON-like payload.' It specifies the verb ('infer'), resource ('candidate Pydantic models'), and input type ('JSON string or JSON-like payload'). However, it doesn't explicitly differentiate from sibling tools like 'generate_json_schema' or 'inspect_type', which might have overlapping functionality.
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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, typical use cases, or compare it to sibling tools such as 'generate_json_schema' (which might generate schemas rather than models) or 'inspect_type' (which might analyze types). This lack of context makes it harder for an agent to choose appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Resolve a Python type annotation or model into a structured description.
| Name | Required | Description | Default |
|---|---|---|---|
| target | Yes | ||
| expand_nested | No |
| Name | Required | Description |
|---|---|---|
| result | No | |
| artifacts | No | |
| diagnostics | No | |
| resolved_target | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action 'resolve' but doesn't clarify if this is a read-only operation, what permissions might be needed, or any side effects like caching or rate limits. It hints at output structure but lacks details on format or error handling, making it insufficient for a mutation tool with zero annotation coverage.
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 a single, efficient sentence that front-loads the core purpose without unnecessary words. It avoids redundancy and wastes no space, making it easy for an agent to parse quickly. Every word earns its place in conveying the tool's function.
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 has an output schema, the description doesn't need to explain return values, which helps completeness. However, with 2 parameters at 0% schema coverage and no annotations, the description lacks details on inputs and behavioral traits. It's minimally adequate but has clear gaps in guiding usage and understanding parameters.
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 for undocumented parameters. It doesn't explain what 'target' represents (e.g., a string of Python code or a model name) or what 'expand_nested' does (e.g., whether it recursively inspects nested types). No parameter details are provided, failing to add meaning beyond the bare 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 verb 'resolve' and the resource 'Python type annotation or model', specifying what the tool does. It distinguishes from siblings like 'explain_model' or 'generate_json_schema' by focusing on structured description rather than explanation or schema generation. However, it doesn't explicitly differentiate from 'list_models' or 'validate_data', keeping it from a perfect score.
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?
No guidance is provided on when to use this tool versus alternatives. It doesn't mention prerequisites, context, or exclusions, such as when to choose 'inspect_type' over 'explain_model' or 'generate_json_schema' for similar tasks. The description lacks any usage context, leaving the agent to infer based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Discover exported Pydantic models in configured packages.
| Name | Required | Description | Default |
|---|---|---|---|
| packages | No | ||
| filter | No |
| Name | Required | Description |
|---|---|---|
| result | No | |
| artifacts | No | |
| diagnostics | No | |
| resolved_target | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It mentions 'configured packages' which hints at some setup dependency, but doesn't explain what 'exported' means, whether this requires specific permissions, what format the output takes, or any limitations like pagination or rate limits.
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 a single, efficient sentence that gets straight to the point without any wasted words. It's appropriately sized for a tool with two parameters and an output schema.
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 has an output schema (which handles return values) and only two parameters, the description is minimally adequate. However, with 0% schema description coverage and no annotations, it leaves significant gaps in understanding parameter usage and behavioral context that could be addressed.
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 for undocumented parameters. It mentions 'configured packages' which loosely relates to the 'packages' parameter, but doesn't explain the 'filter' parameter at all or provide any details about parameter formats, constraints, or usage examples.
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 action ('Discover') and resource ('exported Pydantic models in configured packages'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'explain_model' or 'inspect_type', which might also involve model discovery or inspection.
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 no guidance on when to use this tool versus alternatives. With siblings like 'explain_model' and 'inspect_type' that might overlap in functionality, there's no indication of when this listing tool is preferred over those more specific tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Analyze a snippet or model source for common Pydantic v1-to-v2 migration issues.
| Name | Required | Description | Default |
|---|---|---|---|
| code | No | ||
| target | No | ||
| apply_fixes | No |
| Name | Required | Description |
|---|---|---|
| result | No | |
| artifacts | No | |
| diagnostics | No | |
| resolved_target | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions analysis for migration issues, it doesn't specify whether this is a read-only operation, what the output format is (though an output schema exists), potential side effects, or performance considerations. For a tool with 3 parameters and no annotation coverage, this is insufficient.
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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy for an agent to parse quickly.
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 complexity (3 parameters, migration analysis), lack of annotations, and 0% schema coverage, the description is incomplete. It doesn't explain parameter usage or behavioral details. However, the presence of an output schema reduces the need to describe return values, preventing a lower score.
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 for undocumented parameters. It doesn't explain what 'code', 'target', or 'apply_fixes' mean, their expected formats, or how they interact. The description adds no parameter semantics beyond the schema, failing to address the coverage gap.
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's purpose: 'Analyze a snippet or model source for common Pydantic v1-to-v2 migration issues.' It specifies the verb (analyze), resource (snippet/model source), and scope (Pydantic v1-to-v2 migration issues). However, it doesn't explicitly differentiate from sibling tools like 'compare_validation_modes' or 'explain_model', which prevents a perfect score.
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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context for migration analysis, or how it differs from siblings like 'inspect_type' or 'validate_data'. This leaves the agent without clear usage instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Best-effort parse partial JSON, then validate the parsed fragment.
| Name | Required | Description | Default |
|---|---|---|---|
| target | Yes | ||
| partial_json | Yes | ||
| allow_partial | No |
| Name | Required | Description |
|---|---|---|
| result | No | |
| artifacts | No | |
| diagnostics | No | |
| resolved_target | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'best-effort' parsing and validation, hinting at non-guaranteed success, but lacks details on error handling, performance, or output format. For a tool with no annotations and an output schema, this is insufficient to inform the agent about operational behavior.
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 extremely concise and front-loaded in a single sentence: 'Best-effort parse partial JSON, then validate the parsed fragment.' Every word contributes to the purpose, with zero waste or redundancy, making it efficient for quick understanding.
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 moderate complexity (3 parameters, no annotations, but an output schema exists), the description is incomplete. It states the core function but lacks usage context, parameter details, and behavioral traits. The output schema mitigates some gaps by defining return values, but overall, the description doesn't provide enough information for effective tool invocation.
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 by explaining parameters. It adds no meaning beyond the schema, failing to clarify what 'target', 'partial_json', or 'allow_partial' represent. With 3 parameters and no schema descriptions, this leaves the agent guessing about input semantics.
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's purpose: 'Best-effort parse partial JSON, then validate the parsed fragment.' It specifies the verb ('parse'), resource ('partial JSON'), and an additional action ('validate'). However, it doesn't explicitly differentiate from sibling tools like 'validate_data' or 'serialize_data', which prevents a perfect score.
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 no guidance on when to use this tool versus alternatives. There's no mention of scenarios where partial JSON parsing is needed, prerequisites, or comparisons to sibling tools such as 'validate_data' for complete JSON validation. This leaves the agent without context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Dump validated data using Pydantic serialization behavior.
| Name | Required | Description | Default |
|---|---|---|---|
| target | Yes | ||
| data | Yes | ||
| output_mode | No | python | |
| by_alias | No | ||
| exclude_unset | No | ||
| exclude_defaults | No | ||
| exclude_none | No | ||
| round_trip | No |
| Name | Required | Description |
|---|---|---|
| result | No | |
| artifacts | No | |
| diagnostics | No | |
| resolved_target | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'Pydantic serialization behavior' but doesn't explain what that entails - whether it's read-only, what permissions are needed, what format the output takes, or any error conditions. The description is too terse to provide meaningful behavioral context.
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 extremely concise at just 7 words. While this brevity comes at the cost of completeness, every word earns its place by mentioning key concepts like 'dump', 'validated data', and 'Pydantic serialization'.
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?
For a tool with 8 parameters, no annotations, 0% schema coverage, and multiple sibling tools, the description is completely inadequate. While an output schema exists, the description doesn't explain what the tool does, when to use it, what the parameters mean, or any behavioral characteristics.
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?
With 8 parameters and 0% schema description coverage, the description provides no information about any parameters. It doesn't explain what 'target', 'data', 'output_mode', or any of the boolean flags mean. The description fails to compensate for the complete lack of parameter documentation in 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 'Dump validated data using Pydantic serialization behavior' is vague about what the tool actually does. It mentions 'dump' and 'serialization' but doesn't specify what resource is being serialized or what the output is. It doesn't clearly distinguish from siblings like 'validate_data' or 'generate_json_schema' which also deal with data processing.
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 no guidance on when to use this tool versus alternatives. With siblings like 'validate_data', 'generate_json_schema', and 'parse_partial_json' that also handle data transformation, there's no indication of when serialization is appropriate versus validation or schema generation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Validate input against a model name or Python type expression.
| Name | Required | Description | Default |
|---|---|---|---|
| target | Yes | ||
| data | Yes | ||
| mode | No | python | |
| strict | No | ||
| context | No |
| Name | Required | Description |
|---|---|---|
| result | No | |
| artifacts | No | |
| diagnostics | No | |
| resolved_target | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool validates input but doesn't explain what happens during validation (e.g., returns validation results, errors, or success status), how it handles invalid data, or any performance or security considerations. This is inadequate for a tool with 5 parameters and an output schema.
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 a single, efficient sentence with no wasted words. It's appropriately sized and front-loaded, clearly stating the core purpose without unnecessary elaboration.
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 has 5 parameters with 0% schema coverage and an output schema exists, the description is incomplete. It doesn't explain parameter semantics or behavioral traits, though the output schema may cover return values. For a validation tool with multiple parameters, more context is needed to guide effective use.
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?
The schema description coverage is 0%, so the description must compensate by explaining parameters. It mentions 'model name or Python type expression' (hinting at the 'target' parameter) and 'input' (hinting at 'data'), but doesn't cover 'mode', 'strict', or 'context', nor does it clarify the meaning or usage of any parameters beyond vague hints.
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's purpose: 'Validate input against a model name or Python type expression.' It specifies the verb (validate) and the resource (input), though it doesn't explicitly differentiate from sibling tools like 'compare_validation_modes' or 'inspect_type'.
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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'compare_validation_modes' for comparing validation methods or 'inspect_type' for type inspection, leaving the agent with no context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.0compare_validation_modescreate_example_payloadexplain_modelgenerate_json_schemagenerate_model_from_jsoninspect_typelist_modelsmigrate_v1_to_v2parse_partial_jsonserialize_datavalidate_dataEach tool has a clearly distinct purpose with no overlap: compare_validation_modes analyzes validation behavior differences, create_example_payload generates test data, explain_model creates human-readable documentation, generate_json_schema produces JSON Schema, generate_model_from_json infers models from JSON, inspect_type resolves type annotations, list_models discovers available models, migrate_v1_to_v2 handles version migration, parse_partial_json processes incomplete JSON, serialize_data handles data serialization, and validate_data performs validation. The descriptions clearly differentiate their specific functions.
The naming follows a consistent verb_noun pattern throughout (e.g., compare_validation_modes, create_example_payload, explain_model) with all tools using snake_case. The only minor deviation is that 'list_models' uses a plural noun while others typically use singular nouns (e.g., 'explain_model'), but this is a small inconsistency that doesn't affect readability or predictability.
With 11 tools, this is well-scoped for a Pydantic-focused server. Each tool serves a distinct purpose in the Pydantic ecosystem (validation, schema generation, migration, serialization, etc.), and none feel redundant or unnecessary. The count aligns perfectly with providing comprehensive coverage for working with Pydantic models and validation.
The tool surface provides complete coverage for Pydantic operations: it includes model discovery (list_models), type inspection (inspect_type), schema generation (generate_json_schema), validation (validate_data, compare_validation_modes), serialization (serialize_data), migration support (migrate_v1_to_v2), example generation (create_example_payload), documentation (explain_model), and even specialized utilities like parsing partial JSON (parse_partial_json) and model inference (generate_model_from_json). No obvious gaps exist for typical Pydantic workflows.
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