research-mentor-rag

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Each tool targets a distinct aspect of the research workflow, but research_route and research_mentor both involve stage retrieval. Their descriptions clarify different purposes: route returns next actions while mentor selects specialist methods, so most agents can differentiate them.
Naming Consistency4/5
All tools share the consistent 'research_' prefix, and most use action-oriented names like bootstrap, route, and mentor. The mix of verb and noun forms (e.g., controller vs. frontier_search) is a minor deviation from a strict verb_noun pattern.
Tool Count5/5
Six tools are well-scoped for a research mentor RAG system, covering initialization, direct query, routing, mentoring, live search, and orchestration. This is a reasonable number that avoids both thinness and bloat.
Completeness4/5
The tool set covers the full workflow from bootstrapping the RAG runtime to answering after a novelty search, including a controller that orchestrates the process. Minor gaps exist, such as no explicit tool for updating the RAG corpus, but core operations are complete.
Average 2.6/5 across 6 of 6 tools scored. Lowest: 1.5/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 commits in the last 12 weeks
- Last stable release on August 12, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
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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

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden for behavioral disclosure. It does not mention side effects, return values, prerequisites, or any operational traits beyond the vague 'combine' statement, leaving the agent without critical safety or behavior information.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence, but it is under-specified rather than concise. It omits essential information, making the brevity a liability rather than an asset.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 parameters, required fields, enum) and the absence of an output schema and annotations, the description is severely incomplete. It does not provide enough context for an agent to correctly invoke the tool or interpret its results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description does not explain any of the three parameters (stage, decision, limit). Although 'stage' appears in the phrase 'stage RAG retrieval', it does not clarify the parameter's meaning, format, or how it relates to the decision parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Combine stage RAG retrieval with 12-Skill specialist method selection' but lacks a clear verb+resource structure. It does not specify what the tool actually does with these components or what output to expect, making it vague and not easily distinguishable from siblings.
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. While sibling tools like research_rag_query and research_route exist, the description does not mention any context, exclusions, or alternative selection 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?
With no annotations provided, the description is solely responsible for disclosing behavior. It states that a retrieval runs and returns a result, but it doesn't mention permissions, side effects, reversibility, or what 'correction' entails. This leaves critical behavioral aspects undisclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is brief and front-loaded with a verb. However, it uses unexplained jargon ('multi-head stage retrieval') and lacks substance, making it under-specified rather than appropriately concise. It is short but not informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 3 parameters, no output schema, and no annotations, the description should do heavy lifting. It fails to clarify what the parameters mean, what the returned 'action/correction' looks like, or how the tool fits into a workflow. Major gaps remain in even basic understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
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 by explaining the parameters. It provides no information about 'stage', 'decision', or 'limit' — not even a hint of how they influence the tool's behavior. The agent cannot infer what values to provide or why.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies a verb ('Run') and a resource ('multi-head stage retrieval'), and mentions a return value ('next action/correction'). However, 'multi-head stage retrieval' is jargon that isn't explained, and it doesn't clearly differentiate from sibling tools like research_controller or research_mentor. The overall purpose is only loosely conveyed.
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 given about when to use this tool versus alternatives. The word 'mandatory' hints at a required step but doesn't clarify the context or exclusions. There is no mention of alternatives or 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?
Since no annotations are provided, the description must carry the full burden of disclosing behavior. It reveals the mandatory live frontier search step, which is useful. However, it doesn't explain what 'run mentor RAG' entails, whether there are side effects, rate limits, or what the output looks like. This leaves significant behavioral uncertainty.
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?
A single, efficient sentence that packs several key behaviors without fluff. It is concise and front-loaded, though the dense jargon could be clearer.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
As a controller tool that orchestrates other research tools, the description lacks a holistic explanation of the workflow, its relationship to sibling tools, and expected outputs. With no output schema and minimal behavioral disclosure, it's insufficient for an agent to fully understand when and how 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?
Schema description coverage is 0%, so the description must compensate. It only alludes to research stage auto-detection, which hints at the 'stage' parameter, but it doesn't clarify the enum values, nor does it address 'prompt' or 'limit' at all. The 'limit' parameter is entirely unexplained.
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 states a specific composite action: auto-detecting a research stage, running mentor RAG, and requiring live frontier search. This goes beyond a tautology and gives a clear sense of the tool's role. It distinguishes itself from siblings by being a controller/orchestrator, though it doesn't explicitly mention them.
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 explicit guidance on when to use this tool versus alternatives. The description implies it's for a full research pipeline, but it doesn't say 'use this for complete workflows' or contrast with simpler tools like research_route. An agent would have to infer usage context.
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 carries full responsibility for disclosing behavior. It mentions 'embedding-free' and 'local', but doesn't state whether the operation is read-only, what the query does exactly, or what limitations or side effects exist.
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 a single, focused sentence with no wasted words, front-loading the core action and resource. However, it may be slightly too terse, sacrificing important context for brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Without an output schema or annotations, the description must explain what the query returns and how parameters influence the result. It doesn't, and the sibling tool list doesn't compensate for this missing context. The description is too minimal for a tool with three parameters and no output schema.
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%, and the description adds no explanations for the parameters 'text', 'kind', or 'limit'. The agent must guess what 'text' should contain and what 'kind' controls, leaving a significant semantic gap.
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's action ('Query') and resource ('local embedding-free 1200-paper RAG'), making the purpose unmistakable. It doesn't explicitly differentiate from sibling tools, but the resource specification is distinct enough to separate it from general research tools.
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?
There is no guidance on when to use this tool versus alternatives like research_frontier_search or research_controller. The description only states what it does, not the scenario in which it should be selected.
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 carry the behavioral burden. It mentions 'live' and 'dated' but does not disclose whether this makes network calls, potential rate limits, side effects, or the structure of the returned data. This is insufficient for a tool hitting external APIs.
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 a single, front-loaded sentence with no wasted words. It is appropriately concise, but its extreme brevity sacrifices essential usage and behavior context, preventing a top score.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description should explain return values and search behavior more fully. It only hints at outputs ('papers, topics, gaps') without detailing structure, pagination, or how 'nearest' is determined. The tool's role within the sibling suite is also unclear.
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 coverage is only 33% (from_date documented). The description adds minimal parameter context, implying 'dated' relates to from_date and 'nearest' relates to limit, but it does not explicitly explain query semantics or limit constraints. It fails to compensate for the undocumented query and limit 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 clearly states the tool's function: running a 'dated live OpenAlex+Crossref frontier search' for 'nearest papers, topics and gaps.' It names specific external sources and output types, making it distinct from sibling tools like research_rag_query.
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. It does not mention prerequisites, exclusions, or scenarios that would favor this over research_bootstrap or research_route.
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 provided, the description carries the full burden of behavioral disclosure. It states what it does ('Load the compact 1200-paper RAG runtime and L0-L5 workflow status') but does not explain side effects, resource intensity, whether it is idempotent, or what the output looks like. This is a significant gap for a tool that likely has operational implications.
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, front-loaded sentence with no filler. Every word adds value, precisely conveying the tool's purpose in a compact form.
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 tool's low complexity (no parameters, no output schema), the description is somewhat adequate but leaves gaps. It does not explain what 'L0-L5 workflow status' means or how the agent should use this status. It also does not provide context on how this bootstrapping fits with sibling tools. While not incomplete, it would benefit from a brief note on when to call it or what to expect.
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 input schema is empty with zero parameters, so there is nothing that needs explanation. The description adds no parameter information, but none is required. The baseline for zero parameters is 4, as the description does not need to compensate for missing schema fields.
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 ('Load') and specific resources ('compact 1200-paper RAG runtime' and 'L0-L5 workflow status'), making its function clear. It also distinguishes itself from sibling tools like research_rag_query and research_route, which are query/route operations, whereas bootstrap is an initialization action.
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 this tool is for bootstrapping or setup, but it does not explicitly state when to use it relative to alternatives. It suggests it might be called before other research operations, but there is no explicit 'use this before querying' guidance or mention of 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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