gigaxity-deep-research

Server Quality Checklist

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Latest release: v0.6.1
Disambiguation5/5
Each tool has a distinct purpose: ask for quick answers, discover for exploration, reason for chain-of-thought, research for full pipeline, search for raw results, and synthesize for post-gathering analysis. No significant overlap.
Naming Consistency5/5
All tool names are single, lowercase verbs: ask, discover, reason, research, search, synthesize. Very consistent pattern.
Tool Count5/5
6 tools is ideal for a research server, covering all key operations without being excessive or sparse.
Completeness5/5
The set covers the full research workflow from exploration to synthesis, including raw search, quick answers, deep reasoning, and full pipeline. No obvious gaps.
Average 4/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
- 48 commits in the last 12 weeks
- No stable releases found
- 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.
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How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description bears full burden. It discloses the tool's function (gap analysis, identifying known/unknown) but omits behavioral traits like required permissions, rate limits, or side effects. The description adds moderate value but lacks depth expected for a complex tool.
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?
Very concise at three lines with a clear structure: one-line summary, one-line outcome, one-line usage guidance. No wasted words. However, it could be slightly more informative without losing brevity (e.g., hinting at output or key parameters).
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 five parameters and an output schema, the description is minimal. It mentions the core function and use case but does not explain the role of parameters like focus_mode or identify_gaps. The output schema likely covers return values, so this is acceptable but not exemplary.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description does not elaborate on any parameters; it relies entirely on the schema. While sufficient, it adds no extra meaning beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it performs 'exploratory discovery with knowledge gap analysis' and 'identifies what's known and unknown about a topic'. The verb+resource+outcome is specific and distinct from siblings like search (which returns results) and ask (Q&A). Mention of 'cold-start exploration' reinforces its unique use case.
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?
Says 'Use for cold-start exploration', providing a clear when-to-use scenario. However, it does not specify when not to use or offer alternatives among siblings (e.g., if you need direct answers, use ask; if you need detailed research, use research). The guidance is present but minimal.
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 carries the full burden of behavioral disclosure. The description only states the purpose without detailing side effects, safety, or any behavioral traits. For instance, it doesn't indicate whether the tool is read-only, whether it modifies any state, or what the synthesis process entails (e.g., uses LLM calls). This is a significant gap.
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 at two sentences. The first sentence captures the primary purpose, and the second provides direct usage context. Every word is earned; no fluff.
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 complexity (6 parameters, output schema exists) and lack of annotations, the description is minimal. The output schema covers return values, so that's not an issue. However, the description does not explain the synthesis process, any prerequisites (other than having sources), or how to handle the output. It is adequate but lacks depth in behavioral context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema itself fully documents the parameters. The tool description does not add any additional meaning beyond what the schema already provides. Thus, the baseline score of 3 is appropriate.
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 ('Synthesize pre-gathered content') and outcome ('into coherent analysis'). It distinguishes from sibling tools by explicitly mentioning that it works with already-gathered sources, which sets it apart from search or discover.
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 provides explicit guidance: 'Use when you already have sources from other tools.' This tells the agent when to invoke it, but it doesn't explicitly mention when not to use it or provide alternatives. Given the sibling tools, the guidance is sufficient but could be more comprehensive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It notes 'quick' and 'direct response from model knowledge' but lacks disclosure on latency, cost, accuracy, or other behaviors. Minimal behavioral context beyond 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?
Extremely concise: two sentences plus a header. Front-loaded with core purpose. 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?
Given existing output schema (not shown), description need not explain returns. It covers purpose, usage context, and contrasts with siblings. Lacks mention of error handling or limitations, but adequate for a simple tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline 3 applies. Description adds no extra context beyond schema: no explanation of context role or API key override, but schema already describes them adequately.
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 'Quick conversational answer using LLM' and 'No search, direct response from model knowledge,' distinguishing it from sibling tools like search and research. It specifically targets simple factual questions or follow-ups.
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 advises use for 'simple factual questions or follow-ups' and contrasts with 'No search,' implying not for external discovery. However, it does not explicitly state when not to use or list alternative tools.
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 the pipeline steps (search, aggregation, synthesis, citation formatting), providing good behavioral insight. It does not mention potential costs or API key requirements, but the pipeline description is sufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences: the first is a clear title-like statement, the second outlines the pipeline. No wasted words, front-loaded with key 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 the existence of an output schema (context signal), the description does not need to detail return values. It explains the workflow and sufficiently differentiates from siblings, though it could mention potential dependencies like network access.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds context about the pipeline (e.g., top_k affects per-source results) but does not elaborate on individual parameters beyond what is in the 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 it's a 'Full research pipeline' combining search and synthesis with citations, and lists the pipeline steps. This distinguishes it from sibling tools like 'search' (just search) and 'synthesize' (just synthesis).
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 use for comprehensive research tasks but does not explicitly state when to use this tool vs. alternatives like 'search' or 'synthesize'. 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, so description must cover behavior. Mentions RRF ranking, three sources, no LLM call. Lacks details like result limits or permissions but is adequate for a read search tool.
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 short, information-dense sentences. Front-loaded with key purpose. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Has output schema, so return details not needed. Explains core purpose and differentiation. Could mention more about result structure or limitations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, baseline 3. Description adds no extra meaning to parameters 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?
Clearly states multi-source search with RRF, no synthesis, no LLM call. Verb and resource explicit. Distinguishes from siblings like synthesize.
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?
States 'Use for raw search results without synthesis', providing clear context. Could be more explicit about when not to use or alternatives.
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?
Despite no annotations, the description discloses important behavioral traits: mode switching based on 'sources', that 'reasoning_depth' is ignored in sources-aware mode, and the fixed prompt nature in that mode. It also mentions the per-request API key override. Could be improved by explicitly stating it is a read-only operation with no side effects.
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?
Well-structured with clear mode breakdowns, using bullet-like formatting for readability. Every sentence provides necessary information, no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema, the description doesn't need to detail return values. It covers the two modes, parameter behavior, and alternative tools. Missing aspects like error handling or rate limiting, but overall sufficient for the tool's complexity.
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 100%, baseline 3, but the description adds significant context: explains the two modes and how parameters behave differently in each (e.g., 'context' and 'reasoning_depth' only in no-sources mode, 'sources' switches modes). This greatly enriches the agent's understanding.
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 defines the tool's purpose: deep reasoning with chain-of-thought analysis, with two distinct modes. It distinguishes itself from the sibling tool 'synthesize' by noting that for style variants over pre-gathered sources, one should call 'synthesize' directly.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use each mode: no-sources mode for problems requiring reasoning without external evidence, sources-aware mode when sources are provided. Also provides an alternative: 'For style variants over pre-gathered sources, call synthesize directly.'
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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