BumpGuard

by appcreationsca

Overview Schema Related Servers Score Discussions

Python

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Server Quality Checklist

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Latest release: v0.2.1
Disambiguation5/5
Each tool targets a distinct operation: checking imports, upgrade impact, version diffs, language listing, symbol listing, and snippet verification. Descriptions explicitly differentiate overlapping use cases, e.g., diff_versions vs. check_upgrade.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case (e.g., check_import, list_symbols, verify_snippet), making the set predictable and easy to navigate.
Tool Count5/5
Six tools cover the core concerns of dependency safety and API verification without redundancy or excessive granularity. The scope is well-matched to the server's purpose.
Completeness4/5
The tool surface covers import checking, upgrade impact, API diffing, symbol discovery, and snippet verification. Minor gaps exist (e.g., batch checks or automated fixes), but the core workflow for safe dependency bumps is complete.
Average 4.2/5 across 6 of 6 tools scored. Lowest: 3.5/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 19 commits in the last 12 weeks
- Last stable release on June 18, 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.
This repository includes a glama.json configuration file.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral traits. It only states the tool lists providers, without mentioning safety, data freshness, or any potential side effects. Minimal transparency.
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 a single concise sentence with no unnecessary words. It is front-loaded and directly communicates the tool's function, earning its place.
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 the low complexity (no parameters, no output schema), the description is adequate but lacks details about the list's format or what 'ecosystem providers' entails. Contextual completeness is moderate.
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, so the description adds sufficient meaning beyond the empty schema. Baseline for 0 params is 4, and the description effectively communicates the purpose without additional param details.
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 lists ecosystem providers BumpGuard supports, using a specific verb and resource. It distinguishes from sibling tools like check_import and check_upgrade which perform different actions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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, nor any prerequisites or context. For a simple list tool, usage is somewhat implied but not explicitly stated.
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?
No annotations provided. The description says it checks installation and suggests names but does not specify side effects, return format, or auth needs. The behavior is partially transparent but lacks detail.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with front-loaded purpose. No wasted words. Highly concise and well-structured.
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 simple check tool with no output schema, the description covers purpose and usage. Missing return value explanation, but overall adequate given the tool's simplicity.
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?
Schema description coverage is 0%. The description mentions 'package' and 'language' only implicitly via 'Check whether a package is installed' and default language (Python) is not stated. Does not explain parameter formats or options.
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 checks if a package is installed and suggests close names if not. This distinguishes it from siblings like 'check_upgrade' and 'diff_versions'.
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 advises to use this before writing an import to avoid hallucinated or typo'd package names, providing clear when-to-use guidance.
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?
No annotations are provided, so the description carries the full burden. It discloses that no code scan occurs and defaults to the installed version, which is helpful. However, it does not mention any other behavioral traits like side effects, required permissions, or rate limits, leaving some transparency gaps.
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 extremely concise with only two sentences. The first sentence clearly states the purpose and a key constraint, and the second provides usage guidance. No extraneous 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 moderate complexity (4 parameters, no output schema), the description covers the core functionality, usage guidance, and a behavioral note (no code scan). It could elaborate on the output format, but it is largely sufficient for an agent to decide when to invoke it.
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 schema description coverage is 0%, and the description text adds minimal information about parameters beyond implying that from_version defaults. It does not explain the meaning of 'language' or the format of 'to_version'. The description adds little value over the raw schema for understanding parameters.
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 uses a specific verb ('list'), identifies the resource ('API changes between two versions of a package'), and distinguishes from sibling tools like check_upgrade by adding '(no code scan)'. This clearly defines the tool's scope.
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 ('understand a library's breaking changes in the abstract — e.g. when planning a migration') and when not to ('For 'what breaks in my code', use check_upgrade instead'). Also mentions the default baseline behavior, providing complete guidance.
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?
Without annotations, the description provides key behavioral context: it lists the real API, fetches for a specified version without installing, and allows optional filtering. It does not mention performance or side effects, but as a read-only operation, the disclosure is adequate.
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, each adding distinct value: core purpose, usage context, and optional filtering. It is front-loaded and concise with no wasted 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 complexity (4 parameters, no output schema, no annotations), the description covers the main purpose, usage, and two parameters. Missing the 'language' parameter explanation is a gap, but overall it provides sufficient context for an agent to use the tool effectively.
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 description coverage is 0%, so the description must compensate. It adds meaning for 'version' (fetched without installing) and 'name_filter' (substring of dotted path) but does not explain 'language'. This partial compensation results in a mid-range score.
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 lists the real public API (functions, classes, methods, signatures) of a package, emphasizing it avoids guessing. It distinguishes from sibling tools like check_import, check_upgrade, etc., which perform different actions.
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 use this tool to discover the correct API instead of guessing, and notes it works for the installed version or a specific version. It does not explicitly exclude use cases or mention alternatives, but the context is clear enough for an agent.
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, the description carries full burden. It discloses that the tool performs static analysis, flags missing packages with typo/slopsquat suggestions, and checks attributes/methods on installed modules. It does not mention any destructive actions or side effects, but the tool appears read-only. A small gap is the lack of mention about whether the code is executed or not, but the static analysis statement covers that.
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 concise with two short paragraphs. The first sentence is bold and informative. Every sentence adds value, but the second paragraph could be slightly more structured. Still, it is well-organized and front-loaded with the main purpose.
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 only two parameters and no output schema, the description covers what the tool checks (imports, API calls), how it reports (flags, suggestions), and interpretation guidance. It is complete enough for an agent to use the tool effectively, though it does not specify the return structure.
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?
Schema description coverage is 0%, but the description does not explain the parameters (code and language). It implies the tool takes code but does not describe the format, expected size, or the possible values for 'language' (only mentions default as python). This leaves the agent to infer parameter usage from the schema alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with 'Verify code against the ACTUALLY-INSTALLED packages to catch hallucinations,' clearly stating the verb (verify), resource (code against installed packages), and the problem it solves (hallucinations). It distinguishes from siblings like 'check_import' and 'check_upgrade' by focusing on verifying entire code snippets against the environment.
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 says 'Call this after generating code to check that the imports and API calls it uses really exist in this environment,' providing a clear when-to-use scenario. It also adds interpretation guidance: 'Static analysis only — treat 'medium' findings as 'verify', not 'definitely wrong'.' This helps the agent understand the tool's limitations.
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 carries full burden. It discloses key behavioral traits: static analysis, extraction of public API, diffing, scanning user code, and a disclaimer that 'no findings' is not a guarantee. This fully informs the agent of the tool's 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured: a one-line intro, a short paragraph on usage, a bulleted list of arguments, and a returns note. Every sentence adds value, and there is no redundant or verbose text.
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, no output schema, static analysis), the description covers all necessary aspects: purpose, usage timing, parameter details, return format (report with fields), and limitations. It is sufficiently complete for an agent to select and invoke the tool correctly.
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 clearly explains each parameter in the Args section: package, to_version, code, from_version (optional, defaults to installed), and language (default 'python'). It adds meaningful context beyond the parameter names and types.
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: 'Check what breaks in YOUR code when you upgrade a dependency.' It uses a specific verb-resource combination and distinctly positions itself among siblings like check_import, diff_versions, etc., which are about different aspects of dependency management.
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 advises 'Call this BEFORE bumping a dependency version,' providing clear when-to-use guidance. It also includes a caveat about static analysis limitations, helping set expectations. While it doesn't explicitly list alternatives, the context and sibling names imply when not to use it.
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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