litellm-mcp

Server Quality Checklist

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Latest release: v1.0.9
Disambiguation2/5
The tools' action categories overlap significantly: litellm_admin includes global spend reset and bulk updates (write-like actions), while litellm_execute includes key reset and cache delete (write/delete-like actions). This makes it hard for an agent to choose the correct tool without diving into each tool's internal operation list.
Naming Consistency4/5
All tools share the litellm_ prefix and snake_case, creating a predictable pattern. However, two tools (version, admin) use nouns rather than verbs, deviating from the verb-based pattern of the other four.
Tool Count5/5
Six tools is an appropriate scope for a LiteLLM proxy management server, covering version, read, write, delete, execute, and admin categories without being unwieldy.
Completeness5/5
The tool set provides a broad lifecycle: read, write, delete, and execute actions, plus a dedicated admin tool for global settings. It covers the core proxy management operations, though the underlying operation lists are hidden.
Average 4.1/5 across 6 of 6 tools scored. Lowest: 3.1/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 29 commits in the last 12 weeks
- Last stable release on August 27, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
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.
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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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description explicitly marks the tool as 'destructive, irreversible', which is a key behavioral trait. However, it does not disclose any other side effects, permission requirements, or error behavior beyond the single example.
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 relatively short and front-loads the destructive nature. The example is helpful, though it could be more tightly structured. Unnecessary repetition of 'litellm_delete' could be removed.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and low schema coverage, the description should provide more context about return values, error handling, and complete parameter semantics. The current description lacks this completeness, making it insufficient for complex usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% parameter description coverage, so the description must compensate. It mentions 'operation' and 'params' but only through a single example; it does not explain the structure of 'params' or specify valid operations beyond the hint to use 'help'. This leaves the agent with insufficient guidance for correct invocation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool deletes LiteLLM resources and mentions cache flushall, making the purpose concrete. However, it does not enumerate the specific resource types that can be deleted, leaving room for ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains how to list available operations with 'help' and provides an example call, which guides usage. But it does not differentiate from sibling tools like litellm_read or litellm_execute, missing a clear when-to-use vs alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must carry full burden. Mentions 'reversible actions' but includes 'targeted cache delete', implying some irreversibility. Lacks details on side effects, permissions, rate limits, or error handling. Important behavioral traits are missing.
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 plus an example. First sentence immediately states purpose, second gives help mechanism, third example. No unnecessary words or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 2 params, no output schema, and no annotations, the description covers the broad purpose and usage pattern but lacks information about return values, error handling, and a full list of operations. Adequate but leaves gaps.
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 0%, so description must compensate. Describes 'operation' as a string that can be 'help' or an operation name, and 'params' as a JSON object. Provides an example. However, does not enumerate common operations or parameter structures beyond the example, leaving the agent dependent on calling help.
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 states specific verb+resource ('Execute reversible actions on LiteLLM resources') with concrete examples (block/unblock, key regenerate/reset, cache delete). Clearly distinguishes from siblings like litellm_read (read-only) and litellm_admin (administration).
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?
Provides explicit guidance to call with operation='help' to discover operations and an example invocation. Does not explicitly exclude alternatives or state when not to use, but sibling names and purpose make context clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses the non-destructive nature and create/update semantics, but doesn't detail permissions, idempotency, or effects on existing resources. With no annotations, more depth would be helpful.
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 plus a clear example; no redundant information. The key points are front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a broad multi-resource tool, the description adequately orients the agent, leveraging the operation='help' mechanism to discover specifics. No output schema exists, but the help command mitigates this gap.
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?
The description explains the operation parameter via the help mechanism and gives an example with params, compensating for the schema's lack of detail. It clarifies that params is a JSON object but doesn't enumerate possible values beyond the example.
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 'Create or update LiteLLM resources (non-destructive)' with a list of resource types, distinguishing this write tool from siblings like litellm_delete and litellm_read. The verb and resource scope are specific.
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?
Provides explicit usage instructions: call with operation='help' to list operations, otherwise pass operation name and params. It doesn't explicitly exclude use cases but the non-destructive label implies when to use vs delete.
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 are provided, so the description carries the burden. It labels the tool as 'high risk' and lists potentially destructive operations (global spend reset, bulk user update), which is upfront about behavioral traits. It does not detail all side effects, but the risk warning 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise: three sentences plus a code example, with no fluff. It front-loads the high-risk purpose and immediately directs the user to the help operation for details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (multiple admin operations), the description provides enough context to start via the help operation and lists example operations. No output schema exists, but the help mechanism compensates. It is adequate for a high-risk admin tool.
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%, so the description compensates by explaining the operation parameter as a command name and params as a JSON object. It provides an example and mentions the help operation, adding meaning beyond the raw 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 identifies the tool as for 'Proxy-global LiteLLM administration' and lists specific operations (allowed IPs, global spend reset, bulk user update), distinguishing it from siblings like litellm_read, litellm_delete, litellm_execute, and litellm_version.
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 instructs to call with operation='help' to list available operations and provides an example, giving a clear entry point. It does not explicitly state when not to use, but the context of high-risk admin operations compared to siblings implies appropriate usage.
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?
With no annotations provided, the description carries the full burden. It transparently declares the tool as 'safe, read-only' and enumerates the types of data accessible. This sufficiently conveys the non-destructive nature, though it lacks details on error handling or access requirements.
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 three sentences plus an example, front-loading the purpose and usage. Every sentence adds value: purpose, discovery method, and example. No redundant words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's dynamic nature (many sub-operations), the description is reasonably complete. It explains how to discover operations via help and provides a working example. It could mention return value format or error behavior, but the limited schema and no output schema make this acceptable.
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 the description adds critical meaning: it explains that 'operation' is the operation name and that 'params' is a JSON object with parameters. The example and the help mechanism provide concrete guidance, compensating for the schema's lack of 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 is for querying LiteLLM proxy data in a safe, read-only manner and lists categories of operations (lists, infos, spend/usage, health, settings reads, etc.). It distinguishes from sibling tools like litellm_admin, litellm_delete, litellm_execute by emphasizing the read-only nature.
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 instructs to call with operation="help" to list all available read operations, providing a clear discovery mechanism. It gives a concrete example (litellm_read(operation="ListKeys")). It does not explicitly state when not to use it, but the read-only context and sibling differentiation imply appropriate usage.
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 bears the burden of behavioral disclosure. It details that the output includes `mcp` (via importlib metadata) and `service` (via GET /health/readiness), and notes specific behavior for LiteLLM v1.93.0 where the payload is {status, db} without a version field. This is comprehensive and accurate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, consisting of two clear sentences. The first sentence states the core purpose, and the second provides necessary technical details without extraneous words. It is well-structured and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite the tool's simplicity, the description covers the expected output fields, their derivation, and version-specific notes. Given the presence of an output schema (which may provide additional structure), the description is complete enough for an agent to understand and invoke the tool correctly.
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?
The tool has zero parameters, and the description adds meaning by explaining the output structure and data sources. According to guidelines, zero parameters yields a baseline of 4, and the description meets this baseline without needing further param 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 explicitly states the tool's purpose: 'Get the MCP server version and the LiteLLM service readiness.' It uses specific verbs ('Get') and resources ('version', 'service readiness'), and clearly distinguishes from sibling tools focused on read, admin, delete, and execute operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implicitly suggests usage for checking server version and health, but it does not explicitly state when to use this tool versus alternatives or provide any exclusions. No explicit usage guidance is given.
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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