Massive Context MCP

Server Quality Checklist

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Latest release: v3.0.1
Disambiguation4/5
Most tools have distinct purposes (load, inspect, filter, chunk, analyze). However, the two setup tools (rlm_setup_ollama and rlm_setup_ollama_direct) are very similar and could confuse an agent, and auto_analyze overlaps somewhat with sub_query in using LLMs.
Naming Consistency5/5
All tools follow a consistent 'rlm_verb_noun' pattern in snake_case. Verbs are descriptive and uniform (load, inspect, list, filter, chunk, get, store, etc.), with no mixing of conventions.
Tool Count5/5
17 tools is well-scoped for managing large contexts, chunking, analysis, and system integration. Each tool serves a clear role; the count is not excessive nor too sparse.
Completeness4/5
Covers the majority of expected operations: loading, inspecting, filtering, chunking, analyzing, storing results, and system checks. Missing an explicit way to delete or combine contexts, but core workflows are covered.
Average 3.9/5 across 17 of 17 tools scored. Lowest: 2.8/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- No commit activity data available
- Last stable release on January 19, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
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.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 provided, so description carries full burden. It only states 'retrieve', but does not disclose if operation is read-only, safety implications, or error handling (e.g., if name not found). 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences, no fluff. Front-loaded with action and resource. Could be clearer by mentioning output or usage scope, but no wasted words.
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?
Has output schema so return values are covered elsewhere, but description lacks usage guidelines and behavioral transparency. For a simple one-param tool, it's adequate but leaves gaps for an AI agent to understand when and how to use it effectively.
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?
Input schema has 0% description coverage. Description barely adds: 'name: Result set identifier' only repeats the parameter name with a generic label. No details on format, allowed values, or constraints beyond the schema.
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?
Description clearly states 'Retrieve stored results for aggregation', indicating action and resource. However, lacks explicit differentiation from siblings like rlm_get_chunk, and 'stored results' is somewhat vague.
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?
No guidance on when to use this tool versus alternatives like rlm_store_result or rlm_get_chunk. The description does not mention context, prerequisites, or exclusions.
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 are provided, so the description must disclose behavioral traits. It only mentions storing but omits details like overwrite behavior, size limits, idempotency, or any required context. This is insufficient for safe invocation.
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 with a clear docstring format, immediately stating the purpose and listing parameters with brief explanations. No extraneous content.
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 presence of an output schema (not shown), return value explanation is not required. However, the description lacks behavioral context (e.g., side effects, dependencies) and usage guidelines, making it only partially complete for a tool with 3 parameters.
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?
With 0% schema coverage, the description adds meaningful context: 'name: Result set identifier', 'result: Result content to store', 'metadata: Optional metadata about this result'. This clarifies each parameter's role, though additional constraints (e.g., format, length) are missing.
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 'Store a sub-call result for later aggregation', which identifies the verb and resource. However, it does not differentiate from sibling tools like rlm_get_results or rlm_sub_query, though the purpose is generally clear.
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?
No guidance on when to use this tool versus alternatives. There is no mention of prerequisites or context where storing is appropriate, leaving the agent without clear decision criteria.
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 are provided, so the description must disclose side effects and behavior. It only states that metadata is returned without content, but omits whether this is a destructive operation, idempotent, or requires permissions.
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 sentences, front-loaded with the main purpose, and no unnecessary words. Efficient and clear.
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 presence of an output schema, the description does not need to detail return values. However, it lacks usage context and behavioral details, making it only partially complete for effective use.
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?
The description adds basic meaning to the two parameters ('Identifier' for name, 'full context content' for content), but does not provide constraints, examples, or format requirements. Schema coverage is 0%, so some compensation is needed.
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 action ('Load a large context as an external variable') and key distinction ('Returns metadata without the content itself'), which differentiates it from siblings like rlm_chunk_context or rlm_filter_context.
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?
No guidance on when to use this tool versus alternatives. It does not mention prerequisites, context, or exclusions for usage.
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?
With no annotations, the description must fully disclose behavior. It mentions parallelism and concurrency limits but fails to indicate side effects (e.g., whether results are stored), idempotency, or prerequisites like context loading. The output schema is present, so return values are covered, but behavioral traits are underexplained.
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 very concise: one sentence for purpose and a bullet-style list of parameters. No extraneous information; every sentence is necessary. 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.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the existence of an output schema and sibling tools, the description covers the core functionality and parameter details well. However, it lacks mention of prerequisites (e.g., context must be loaded) and error handling in parallel processing, which would enhance completeness.
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?
The schema description coverage is 0%, so the description carries the full burden. It provides clear explanations for all 6 parameters, including default values, allowed values (e.g., provider options), and clarification on model defaults. This adds significant value beyond the raw schema.
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 processes multiple chunks in parallel, but it does not explicitly distinguish it from the sibling tool 'rlm_sub_query', which likely handles single sub-queries. The inclusion of 'batch' in the name and 'multiple chunks' provides some differentiation but not explicit contrast.
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?
No guidance is provided on when to use this tool versus alternatives like 'rlm_sub_query'. The description only states what the tool does, without context on appropriate scenarios or exclusions.
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?
With no annotations, the description must disclose behavioral traits; it mentions automatic content detection and chunking but does not clarify whether the tool mutates state, requires specific permissions, or has side effects. The 'optimal chunking strategy' is vague, and no mention of output behavior or error handling is provided.
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, leading with the core purpose and using a clear list for parameters. Every sentence adds information, though the parameter list could be more structured (e.g., docstring format) but is still effective.
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?
While the output schema exists (not shown), the description fails to connect the tool's results to sibling tools like rlm_store_result or rlm_get_results, leaving the workflow unclear. For a complex analytical tool, the description should explain how to handle outputs or integrate with other steps.
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 all five parameters beyond the schema: name as context identifier, content as the material, goal with enumerated options, provider with default and preference, and concurrency with default and max. This adds significant value given 0% schema coverage.
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 automatically detects content type and analyzes with an optimal chunking strategy, specifying common tasks via the goal parameter. This distinguishes it from sibling tools like rlm_chunk_context or rlm_sub_query, which have different purposes.
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 provides a list of possible goals and hints at one-step analysis, but it does not explicitly explain when to use this tool instead of siblings like rlm_filter_context or rlm_exec. Usage context is implied but not definitive.
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 are present, so the description must disclose behavioral traits. It mentions 'creates a new filtered context', implying the original is unchanged, but does not specify error handling for invalid regex, input size limits, or whether the operation is reversible. Minimal beyond basic purpose.
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 at two sentences plus a bullet-style Args list. It front-loads the purpose and then details parameters efficiently with no unnecessary 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 has an output schema (though not shown), return values need not be explained. The description covers the core purpose and parameters. However, it lacks details on how to use the result (e.g., accessing the new context) and error scenarios, but overall is sufficient for a filter 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 must compensate. It provides brief explanations for all four parameters (name, output_name, pattern, mode) in the Args section, adding meaning beyond the raw schema. This is adequate but could be more detailed (e.g., pattern format).
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 filters context using regex/string operations and creates a new filtered context. The verb 'filter' combined with resource 'context' is specific, and the tool is distinct from siblings like rlm_chunk_context or rlm_inspect_context.
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?
No guidance is provided on when to use this tool versus alternatives (e.g., rlm_chunk_context for splitting, rlm_inspect_context for viewing). The description only states what it does, not when 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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and description lacks behavioral details such as side effects, required permissions, rate limits, or what happens to data; only lists parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Concise with clear header and bulleted args, no redundant information.
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?
Adequate for parameter listing but lacks behavioral context and integration details with sibling tools; output schema exists so return values not needed, but behavioral transparency gap remains.
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?
Adds meaning for provider (with 'auto' explanation) and chunk_index (optional), but does not explain query or context_name beyond names; schema coverage 0% makes description necessary.
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 makes a sub-LLM call on a chunk or filtered context, distinguishing it from siblings like rlm_sub_query_batch and rlm_filter_context.
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?
Implied usage as 'core of recursive pattern' but no explicit when-to-use or when-not-to-use, nor comparison with alternatives like rlm_sub_query_batch.
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 must convey behavioral traits. It notes that only metadata is returned, not full content, and lists the strategies. However, it does not disclose whether chunking modifies the original context, side effects, or required permissions.
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 with no filler. It front-loads the purpose and follows with parameter details. Every sentence adds value; no redundancy.
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?
The tool has an output schema, so return value details are not needed. The description covers parameters and output type. However, it lacks explicit preconditions (e.g., context must be loaded) and does not differentiate from sibling chunk-related tools. Still, it is adequately complete for a straightforward 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 description coverage is 0%, so the description is essential. It explains 'name' as context identifier, 'strategy' with valid options (lines, chars, paragraphs), and 'size' with context-dependent meaning. This adds significant value beyond the schema's type/default info.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('chunk'), the resource ('a loaded context'), and the output ('chunk metadata, not full content'). It distinguishes from siblings like rlm_load_context and rlm_get_chunk which have different purposes.
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 implies that the context must be loaded before chunking, but does not explicitly state when to use this tool versus alternatives like rlm_filter_context. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Discloses sandboxed subprocess, security firewall check (RLM_FIREWALL_ENABLED=1) and timeout behavior. Does not mention persistence or idempotency, but covers key safety aspects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with Args and Security sections. Purpose sentence is front-loaded. Could be slightly more concise, but no redundant content.
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?
Output schema exists, so return values are not needed in description. However, description lacks detail on error types, success behavior, and handling of large code blocks. Security and timeout are covered, but more context on execution results would improve completeness.
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?
Input schema has 0% description coverage. Description compensates by explaining 'code' requires a result variable, 'context_name' is a previously loaded context, and 'timeout' has default 30. Adds meaning beyond type information.
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 clearly states 'Execute Python code against a loaded context in a sandboxed subprocess', specifying verb, resource, and environment. Distinguishes from sibling tools like rlm_load_context and rlm_store_result by focusing on code execution.
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?
Description implies use when needing to execute custom Python code, but lacks explicit guidance on when to use this tool versus alternatives (e.g., rlm_sub_query, rlm_filter_context). No when-not or comparison provided.
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?
Without annotations, the description implies a read-only operation but provides no additional behavioral details (e.g., cost, pagination, or what 'metadata' includes).
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?
A single, front-loaded sentence with no extraneous words, efficiently conveying the tool's 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 the presence of an output schema and the simplicity of the tool, the description is nearly complete, though it could elaborate on what 'metadata' entails.
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?
No parameters exist, so the schema covers 100%; the description adds no parameter info but is not required to, earning a baseline of 4.
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' and identifies the resource as 'all loaded contexts' with 'their metadata', clearly distinguishing it from sibling tools like rlm_load_context or rlm_filter_context.
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?
No explicit guidance on when to use this tool versus alternatives; usage is implied (list contexts when needed) but lacks exclusions or context.
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 installation method, dependency on Homebrew, potential sudo prompt, and explains each boolean flag. It also suggests model alternatives for lower RAM. However, it does not mention error handling or idempotency, so not a 5.
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, front-loads the purpose, and uses bullet points for args. Every sentence adds value (purpose, prerequisite, pros/cons, parameter details). No unnecessary text.
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 existence of an output schema, return value details are not needed. The description covers prerequisites, pros/cons, and all parameters. Missing some behavioral details like idempotency, but overall complete for a setup tool with 4 params.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must explain all parameters. It clearly explains each boolean flag's action (install, start service, pull model) and the model parameter's default and alternatives, fully compensating for the lack of schema descriptions.
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 installs Ollama via Homebrew on macOS, and lists specific sub-actions. However, it does not explicitly differentiate from sibling tool rlm_setup_ollama_direct, so it falls short of a 5.
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 mentions a prerequisite (Homebrew pre-installed) and lists pros/cons, but does not explicitly state when to use this tool versus alternatives like rlm_setup_ollama_direct. Usage context is present but no exclusion 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 must disclose behavioral traits. The description implies it is a read operation ('Get'), but does not explicitly state lack of side effects, permissions, or other behaviors. It 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: one sentence plus a two-line args list. It is front-loaded with the purpose and contains no filler.
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 retrieval tool with two parameters and an output schema (not shown), the description is complete. It explains the tool's purpose and parameter meanings. The output schema presumably covers return values, so no further explanation needed.
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?
With 0% schema description coverage, the description adds meaning by documenting parameters: 'name: Context identifier' and 'chunk_index: Index of chunk to retrieve.' This clarifies purpose 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 states 'Get a specific chunk by index' and provides context 'Use after chunking to retrieve individual pieces.' It distinguishes itself from sibling tools like rlm_chunk_context (likely creates chunks) and rlm_get_results (likely gets results).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use after chunking to retrieve individual pieces,' indicating when to use it. Although it does not mention when not to use it or alternatives, the guidance is sufficient for a simple retrieval tool.
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, so description carries burden of behavioral disclosure. Description implies a read-only verification but does not explicitly state if any side effects occur. The existence of an output schema partially compensates, but without mentioning return format, transparency is adequate but not enhanced.
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 concise sentences: first sentence states purpose, second lists specifics and usage context. No fluff, well structured, and front-loaded with critical information.
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 no parameters and presence of output schema, description is fairly complete. It explains what the tool checks and when to use it. Could mention the output format, but that is likely handled by the output schema.
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?
Input schema has zero parameters, so description need not add parameter info. Schema coverage is 100%. The description adds no extra parameter information, which is acceptable given no parameters exist.
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 clearly states the tool checks system requirements for Ollama with a specific model, using the verb 'check' and resource 'system requirements'. It distinguishes itself from sibling tools like rlm_ollama_status and rlm_setup_ollama, which deal with status and setup respectively.
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?
Explicitly states 'Use before attempting Ollama setup', providing clear context for when to invoke. Lists what it verifies (macOS, Apple Silicon, etc.), but does not mention when not to use or alternatives, though the sibling tools imply alternatives.
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, so description carries full burden. It states read-only behavior ('without loading full content') but does not mention permissions, side effects, or other traits typical for inspection tools.
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?
Extremely concise: two sentences plus param details. Front-loaded with key purpose. Every sentence adds value with no redundancy.
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?
Tool has output schema so return values likely documented there. Description covers purpose and params adequately for a simple inspection tool. Missing explicit example or sibling differentiation, but complete enough.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, yet description fully compensates by explaining 'name' as context identifier and 'preview_chars' as number of chars to preview with default, adding meaning beyond bare schema 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?
Description clearly states verb 'inspect', resource 'loaded context', and what it does 'get structure info without loading full content'. Distinguishes from siblings by specifying it does not load full content.
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?
Description implies when to use (when structure info is needed without full content) but lacks explicit when-not or alternative tool names. The note about not loading full content provides clear usage context.
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 full burden. It discloses that the firewall is auto-enabled upon installation and describes the return fields. It does not mention side effects (likely none) and is consistent with a read operation.
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 brief, front-loaded with the purpose, and every sentence is informative. It includes a clear return format without unnecessary verbiage.
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 zero parameters and a well-described output schema in the description, the tool definition is completely self-sufficient. It covers what the tool does, what it returns, and how the firewall is enabled.
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?
The input schema has 0 parameters and 100% coverage. The description adds significant value by specifying the exact return structure (enabled, package_installed, ollama_url, etc.) beyond what the schema provides. Baseline for 0 params is 4, and the rich return type warrants a 5.
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 the status of the code execution firewall and lists the specific information returned (enabled, package_installed, ollama_url, etc.). This is a specific verb+resource that distinguishes it from siblings like rlm_ollama_status or rlm_system_check.
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?
It implies usage for checking firewall status but does not explicitly state when to use this versus alternatives like rlm_system_check. No guidance on prerequisites or exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses caching behavior and the force_refresh parameter to bypass cache, adding transparency. No side effects mentioned but none expected for a read-only status check.
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 succinct with three sentences and an Args section, each sentence providing essential information without redundancy. 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?
Given the tool's simplicity and the presence of an output schema, the description covers the key return information: whether running, list of models, and default model availability. Complete for the intended use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description fully explains the sole parameter 'force_refresh' with its purpose and default value, adding meaningful semantics beyond the schema type.
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 Ollama server status and available models. It uses specific verb 'Check' and specific resource 'Ollama server status and available models', distinguishing it from sibling tools like setup commands.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use this to determine if free local inference is available', providing a clear usage context. It does not specify when not to use, but the context with siblings implies alternatives for setup or analysis.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description fully carries the burden. Discloses download source, installation path, PATH setup, auto-update absence, foreground process, and behavior of each argument (e.g., background service).
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?
Structured with header, pros/cons bullet list, and argument list. No superfluous text; each sentence adds value. Front-loaded with purpose and context.
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?
Output schema exists (not shown) so return values need not be explained. Description covers installation, service start, and model pull steps. Minor gap: does not indicate how to verify success or handle errors, but sufficient for typical use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage 0% but description explains each parameter: install, start_service, pull_model (as booleans) and model string with defaults and alternatives (gemma3:12b, gemma3:4b, gemma3:1b). Adds significant meaning beyond 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?
Description clearly states the tool installs Ollama via direct download on macOS, specifying the resource and action. It differentiates from sibling rlm_setup_ollama by noting pros/cons versus Homebrew.
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?
Explicitly lists pros/cons and steps (install, start_service, pull_model). Implies usage scenarios (headless, locked-down, no sudo). Lacks explicit when-not-to-use or direct comparison with sibling rlm_setup_ollama, but implied.
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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