Analyzes git repositories, scoring commits by risk (complexity delta × blast radius × churn × size) and identifying hotspots, providing tools for commit analysis and blast radius assessment.
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., "@Suitable Loop index my codebase and give a summary"
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.
Local production engineering platform — semantic code analysis, git risk scoring, and log correlation via MCP.
Suitable Loop indexes your Python codebase and exposes deep analysis through MCP tools that AI assistants can call:
Code Analysis — AST-based parsing, function/class extraction, call graph construction, cyclomatic complexity
Git Risk Scoring — Weighted risk scores per commit (complexity delta × blast radius × churn × size)
Log Correlation — Parse logs, group errors by signature, map stack frames to indexed code
Dependency Graph — NetworkX-powered call graphs, import trees, blast radius analysis
Related MCP server: agentmako
Create .mcp.json in your project root:
{
"mcpServers": {
"suitable-loop": {
"command": "uvx",
"args": ["suitable-loop"]
}
}
}That's it. uvx fetches the package from PyPI automatically — no manual install needed.
Local development? Point to a local checkout instead:
"args": ["--from", "/path/to/suitable-loop", "suitable-loop"]
Clone the repo and copy the commands into your project:
git clone https://github.com/suitable-adventures/suitable-loop.git /tmp/suitable-loop
mkdir -p .claude/commands
cp /tmp/suitable-loop/.claude/commands/* .claude/commands//onboard # Get a full codebase orientation
/risk-report # Analyze recent commits by risk
/impact-check models.py # Check blast radius before changing a file
/debug-error "ConnectionResetError in db/pool.py"
/health-check # Full codebase health assessment
/trace-function my_function # Map a function's callers and calleesCommand | What it does |
| Index the codebase and produce a full orientation: structure, hotspots, complexity, blast radius |
| Score recent commits by risk, find hotspots, recommend where to focus review |
| Before changing a file: blast radius, who calls what, what could break |
| Correlate an error/traceback to source code paths |
| Full assessment: complexity, coupling, churn trends, actionable recommendations |
| Map a function's callers, callees, complexity, and role in the system |
| Bump version, tag, and publish a new release to PyPI |
Tool | Description |
| Index a Python project — parse AST, extract entities, build call graph |
| Look up a function/class/file by name with all relationships |
| Find all functions that call a given function |
| Find all functions called by a given function |
| Get import dependency tree for a file |
| Full-text search across indexed functions and classes |
| Top N most complex functions |
| High-level stats and most-connected modules |
Tool | Description |
| Score recent commits by risk |
| Deep-dive a single commit |
| Files with high churn × high dependency count |
| Transitive impact of changing a file |
Tool | Description |
| Parse log files, extract errors, group by signature |
| List distinct error groups by frequency |
| Full detail on an error group with code links |
| Map raw error text to code paths |
| Error frequency over time |
Tool | Description |
| Server status and entity counts |
| Incremental re-index (only changed files) |
suitable_loop/
├── __init__.py # Package root
├── __main__.py # Entry point (python -m suitable_loop)
├── server.py # MCP server setup (FastMCP) + main() entry point
├── config.py # Configuration dataclasses
├── models.py # Data models (entities, edges)
├── db.py # SQLite database layer
├── analyzers/
│ ├── code_analyzer.py # AST parsing, call resolution
│ ├── git_analyzer.py # Commit analysis, risk scoring
│ └── log_analyzer.py # Log parsing, error grouping
├── graph/
│ └── engine.py # NetworkX graph engine
└── tools/
├── code_tools.py # MCP code analysis tools
├── git_tools.py # MCP git analysis tools
├── log_tools.py # MCP log analysis tools
└── util_tools.py # MCP utility toolsEnvironment variables:
Variable | Default | Description |
|
| SQLite database path |
|
| Logging level |
Default exclude patterns (files skipped during indexing):
.venv/, venv/, node_modules/, __pycache__/, migrations/, .git/
For working on Suitable Loop itself:
cd suitable-loop
uv venv && uv pip install -e ".[dev]"
uv run pytestGet the import dependency tree for a file, up to N levels deep.
| Name | Required | Description | Default |
|---|---|---|---|
| depth | No | ||
| file_path | Yes |
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 does not state whether the operation is read-only, any side effects, or required permissions. The description only mentions the action but no behavioral traits.
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 sentence, which is concise, but it lacks necessary details. It front-loads the purpose but is too brief to be minimally viable. Could be longer to cover parameters and usage.
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 no output schema, no annotations, and 0% parameter coverage, the description is incomplete. The agent cannot infer return format, error conditions, or the exact behavior of depth. For a tool with 2 parameters, more detail is needed.
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 0% schema description coverage, the description must explain parameters. It only hints at 'depth' via 'up to N levels deep' but does not explain the default value or the meaning of depth. The required file_path parameter is not elaborated beyond its name.
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?
Description clearly states 'Get the import dependency tree for a file', specifying the verb (Get) and resource (import dependency tree). It distinguishes from sibling tools like search_code and find_callers by focusing on dependency tree visualization.
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 provided on when to use this tool versus its siblings. The description lacks context for when a dependency tree is appropriate compared to other code analysis tools, and does not mention any prerequisites or limitations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get full detail on an error group: traceback, affected functions, sample log entries, and timeline.
| Name | Required | Description | Default |
|---|---|---|---|
| error_group_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It lists the return components but does not disclose any behavioral traits such as whether the operation is read-only, authentication requirements, or performance implications. The description is adequate but could be more explicit.
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 sentence that is front-loaded with the main action and delivers key details efficiently. There is no wasted text.
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 lack of output schema, the description adequately lists the return items. However, it does not describe the structure or format of the output. The context of sibling tools is clear, and the description covers the essential purpose well.
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 the description does not explain the lone parameter 'error_group_id'. Although the parameter's purpose is somewhat inferable from context, the description adds no additional meaning beyond the schema. This is a notable 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 verb 'Get' and the resource 'error group', and lists specific details returned (traceback, affected functions, sample log entries, timeline). This distinguishes it well from siblings like 'get_error_groups' which likely lists groups without full details.
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 implies use when detailed information about a specific error group is needed, but it does not explicitly state when not to use it or provide alternative sibling tools for comparison. No usage exclusions are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find all functions that call the given function.
| Name | Required | Description | Default |
|---|---|---|---|
| function_name | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It only states the basic action but does not mention side effects, performance implications, scope (e.g., whole codebase), or whether results are recursive. This is insufficient for a tool that likely performs a heavy operation.
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 sentence, front-loading the purpose. No extraneous information is included, and every word serves a clear function. It is appropriately concise 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 lack of output schema and annotations, the description is too brief. It does not explain the output format (e.g., list of function names with locations), whether results are transitive, or any limitations. For a static analysis tool, this leaves significant gaps for an agent trying to use it correctly.
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 only parameter, function_name, has no schema description (0% coverage). The description adds context by indicating that the function is the one being called, which clarifies the parameter's role. However, it does not specify the format (e.g., fully qualified name) or restrictions, so it provides minimal added value over 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 'Find all functions that call the given function' clearly states the tool's action and resource. It is specific enough to indicate what the tool does, though it does not explicitly distinguish from sibling tools like dependency_tree or search_code, which could also return caller information.
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. No when-to-use, when-not-to-use, or comparison with sibling tools is given. The agent receives no help in choosing between find_callers and tools like dependency_tree or search_code.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all distinct error groups sorted by frequency. Each group includes exception type, occurrence count, and links to affected code.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description implies a read-only listing operation but does not explicitly disclose behavioral traits like authentication requirements, rate limits, or side effects. The lack of such details is a minor gap.
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 short and directly gets to the point, with no redundant sentences. However, the misleading 'all' statement reduces precision.
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?
The description covers return content but omits important context: it does not address pagination or how the limit parameter affects the result set, nor does it describe handling of empty results or error conditions. Given no output schema, more detail was needed.
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 claims 'List all distinct error groups' while the input schema includes an optional `limit` parameter defaulting to 20. This contradicts the 'all' claim, and the description does not add any meaning beyond the schema, failing to explain the parameter's effect.
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 (list), resource (error groups), sorting (by frequency), and output content (exception type, occurrence count, links). It effectively distinguishes from siblings like error_detail which likely focuses on a single group.
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 such as error_detail for details on a specific group or other sibling tools. The description does not mention prerequisites, limitations, or exclusion cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find code hotspots — files that change frequently AND are highly depended upon. These are the highest-risk areas of the codebase.
| Name | Required | Description | Default |
|---|---|---|---|
| n_commits | No | ||
| repo_path | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries the full burden. It states the tool identifies hotspots based on change frequency and dependencies, but does not disclose computational cost, output format, or whether it's read-only.
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 with two sentences, no filler, and front-loads the core purpose.
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?
With 2 parameters and no output schema, the description is too brief—it does not explain parameter usage, return structure, or how to interpret the report, making it incomplete for an agent.
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 coverage is 0% and the description does not explain the purpose of repo_path or n_commits parameters, leaving the agent to guess their meaning.
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 finds code hotspots defined as files that change frequently and are highly depended upon, distinguishing it from siblings like dependency_tree or search_code.
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 implies usage for identifying high-risk areas but lacks explicit guidance on when to use this tool versus alternatives like dependency_tree or error_detail.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Index a Python codebase. Parses all .py files, extracts functions, classes, imports, and call relationships. Builds a semantic graph for querying. Use force=True to re-index even unchanged files.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | ||
| force | No |
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 parses .py files and extracts specific elements, and mentions the force parameter behavior. However, it does not describe side effects (e.g., whether it overwrites an existing index), resource usage, or prerequisites (e.g., Python installation, write permissions). Some behavioral details are provided but not comprehensive.
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 three sentences: first states the core action, second details what is parsed, third gives usage guidance for force. Every sentence serves a distinct purpose, and the key information is front-loaded. No wasted words.
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 no output schema, no annotations, and moderate complexity, the description covers purpose, inputs (path and force), and partial behavior. However, it lacks details about the output (e.g., what is returned or if it is void), potential side effects (e.g., index overwrite), and prerequisites. It is adequate but has 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?
The input schema has 0% description coverage, so the description must compensate. It explains the force parameter ('re-index even unchanged files') but provides no additional meaning for the required path parameter beyond what the schema's title 'Path' implies. The description adds partial semantic value, but the path parameter remains under-documented.
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 indexes a Python codebase, parsing .py files to extract functions, classes, imports, and call relationships. It builds a semantic graph for querying, which differentiates it from sibling tools like search_code or dependency_tree that focus on querying or specific relations.
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 gives a guideline for the force parameter ('Use force=True to re-index even unchanged files'), implying when to use it. However, it does not explicitly state when to use this tool versus siblings (e.g., that indexing is a prerequisite for search_code), nor does it provide exclusions or alternatives. Usage guidance is implied but not explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Parse and store a log file or directory of log files. Auto-detects format (Python stdlib logging or JSON-per-line). Extracts errors, groups them by signature, and maps stack frames to indexed code.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses key behaviors: auto-detection of formats (Python stdlib or JSON-per-line), error extraction, grouping by signature, and stack frame mapping. Lacks details on side effects, but sufficient for a read-like operation.
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?
Two sentences, front-loaded with action verb, no superfluous words. Each sentence adds essential information.
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?
Covers core functionality but omits output description, error handling, and prerequisites. Given one parameter and no output schema, completeness is adequate but not thorough.
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 coverage is 0%, so the description must compensate. It implies 'path' can be a file or directory, but does not clarify allowed formats, absolute vs relative paths, or any constraints. Minimal added value beyond the schema's type.
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 parses and stores log files, auto-detects format, and extracts errors. It distinguishes itself from sibling code-analysis tools by focusing on log ingestion.
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 explicit guidance on when to use this tool versus alternatives like error_detail or get_error_groups. The description does not mention prerequisites or 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.
Full-text search across indexed functions and classes.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| max_results | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; the description only states 'search' without disclosing output details, limitations, or 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence is efficient, but underspecified for a search 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?
Lacks output schema and parameter details, leaving ambiguity about result format and pagination; incomplete.
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% and the description adds no information about parameters like query format or max_results behavior.
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 performs full-text search across indexed functions and classes, distinguishing it from sibling tools like error_detail and index_codebase.
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?
Implies usage for finding functions/classes by text but lacks explicit guidance on when to use vs alternatives or when not to use.
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.
v0.1.3dependency_treeerror_detailget_error_groupshotspot_reportingest_logsquery_entityreindexsearch_codev0.1.3find_callersindex_codebasequery_entityreindexEach tool has a clearly distinct purpose: indexing, searching, querying callers/dependencies, hotspot analysis, log ingestion, and error grouping. No two tools overlap in functionality.
Most tools follow a verb_noun pattern (e.g., index_codebase, find_callers, ingest_logs), but a few are noun_noun (error_detail, dependency_tree, hotspot_report), creating minor inconsistency. Overall pattern is still readable and intuitive.
With 8 tools, the set is well-scoped for a code analysis and debugging server. It covers indexing, searching, dependency analysis, and log-related error tracking without being bloated or insufficient.
The tool set covers core workflows: indexing, searching, dependency analysis, hotspot detection, log ingestion, and error inspection. A minor gap is the lack of a tool to list all indexed functions/classes, but search_code and dependency_tree can compensate.
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