Jij MCP Server

Official

by Jij-Inc

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Python

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes, with clear separation between JijModeling and Qiskit domains. However, there is some potential confusion between 'qiskit_v0tov1v2_migration_guide' and 'learn_jijmodeling' as both are educational guides, and 'qiskit_v1_api_reference_toc' and 'qiskit_v2_api_reference_toc' are very similar in function. The descriptions help clarify, but the boundaries could be slightly ambiguous.
Naming Consistency3/5
The naming is mixed with no consistent pattern. Some tools use snake_case (e.g., 'fetch_as_markdown', 'jm_check'), while others use descriptive phrases with underscores (e.g., 'qiskit_v0tov1v2_migration_guide'). There is no uniform verb_noun structure, and names vary in length and style, making them less predictable.
Tool Count4/5
With 8 tools, the count is reasonable for a server covering JijModeling and Qiskit support. It's slightly on the higher side but manageable, as each tool serves a specific function without obvious redundancy. The scope is well-defined, though it could be streamlined.
Completeness3/5
For the JijModeling domain, there is a check tool and a learning guide, but no creation or execution tools, leaving gaps. For Qiskit, there are checking, tutorial, migration, and API reference tools, which cover educational and reference needs but lack direct code execution or quantum computing operations. The surface is functional but incomplete for full workflow support.
Average 3.4/5 across 8 of 8 tools scored. Lowest: 2.8/5.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
- 0 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
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How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool returns a string guide, which is basic output information, but lacks details on format, length, whether it's static or dynamic, or any operational constraints like rate limits or authentication needs. This leaves significant gaps in understanding how the tool behaves.
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 very concise, consisting of two short sentences that state the purpose and return type. It's front-loaded with the main action and avoids unnecessary details, though it could be slightly more informative without sacrificing 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?
Given the lack of annotations and output schema, the description is incomplete. It mentions the return type is a string but doesn't specify what the guide contains, its structure, or how it might be used. For a tool that presumably provides educational content, more context about the guide's nature would be helpful for an agent to decide 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.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and the schema description coverage is 100% (since there are no parameters to describe). The description doesn't need to add parameter semantics, so it appropriately avoids discussing inputs. A baseline score of 4 is given as it doesn't introduce confusion or redundancy regarding parameters.
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 states the tool 'Provide[s] a guide to JijModeling', which is a clear verb+resource combination. However, it doesn't distinguish this from potential sibling tools that might also provide educational content about JijModeling, making it somewhat vague about its specific role within the server's ecosystem.
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 offers no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, context for usage, or exclusions, leaving the agent to infer usage based solely on the tool name and purpose.
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. It states the tool checks code and returns a dict result, but it does not describe what the check entails (e.g., syntax validation, rule compliance, error types), potential side effects, or any constraints like rate limits or authentication needs. This leaves significant gaps in understanding the tool's behavior.
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 and well-structured, with a clear purpose statement followed by Args and Returns sections. It avoids unnecessary words and is front-loaded with the main function. However, it could be slightly more informative without sacrificing brevity, such as hinting at what the check involves.
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 complexity of code checking, lack of annotations, no output schema, and minimal parameter details, the description is incomplete. It does not explain what the result dict contains, potential error cases, or how to interpret outputs. This inadequacy makes it hard for an agent to use the tool effectively without additional 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?
The description adds minimal semantics beyond the input schema. It specifies that 'code' is a string representing the code to check, which aligns with the schema's type. However, with 0% schema description coverage, it does not compensate by detailing format, examples, or constraints (e.g., code language, length). The baseline is 3 due to the single parameter, but it lacks enrichment.
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 purpose: 'Check the code for JijModeling rules.' It specifies the verb ('check') and resource ('code for JijModeling rules'), making it understandable. However, it does not explicitly differentiate from sibling tools like 'learn_jijmodeling' or 'qiskit_code_static_check', which might involve similar checking or learning functions, so it misses full sibling differentiation.
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 lacks context such as when to check JijModeling code, what scenarios it applies to, or how it differs from siblings like 'learn_jijmodeling' or other code-checking tools. This absence of usage instructions leaves the agent without clear direction.
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. It mentions returning a string with migration guide content, which gives basic output info, but lacks details on behavioral traits like whether it's a static resource, if it requires authentication, rate limits, or how the content is formatted. For a tool with zero annotation coverage, this is a significant gap in transparency.
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 three sentences: it sets up context, states the tool's purpose, and specifies the return type. While generally efficient, the first sentence about AI training is somewhat extraneous and doesn't directly earn its place in a tool description. It could be more front-loaded by starting with the tool's action. However, it's not overly verbose.
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 0 parameters and no output schema, the description provides basic info (returns a string with guide content) but lacks depth. For a tool that likely delivers educational content, it doesn't explain the scope, format, or source of the guide, which could be important for an agent. With no annotations and simple structure, it's minimally adequate but has clear gaps in 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?
There are 0 parameters, and schema description coverage is 100%, so no parameter info is needed. The description doesn't add param semantics, but with no parameters, the baseline is 4 as it doesn't need to compensate for any gaps. It appropriately avoids unnecessary param details.
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 states the tool provides a migration guide from Qiskit v1 to v2, which is a clear purpose. However, it's somewhat vague about the exact resource ('guide content') and doesn't explicitly differentiate from siblings like 'qiskit_v1_api_reference_toc' or 'qiskit_v2_api_reference_toc' that might also help with migration. The mention of 'AI models trained on v0.x' adds context but doesn't sharpen the tool's specific function.
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 usage ('refer to this guide first when writing Qiskit code'), suggesting it's a prerequisite for coding, but doesn't explicitly state when to use this vs. alternatives like the API reference tools or tutorials. It provides some context (for AI unfamiliar with v1/v2) but lacks clear exclusions or named alternatives, leaving usage somewhat inferred rather than explicitly guided.
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. It states the tool fetches and returns the table of contents in Markdown format, which covers the basic operation and output. However, it lacks details on potential behavioral traits such as network dependencies, error handling, rate limits, authentication needs, or whether it's idempotent. For a tool with no annotations, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded, consisting of only two sentences that directly state the action and the return value. Every sentence earns its place by providing essential information without any waste. The structure is clear and to the point, making it easy for an agent to parse quickly.
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 complexity (simple fetch operation with 0 parameters) and the absence of annotations and output schema, the description is minimally complete. It explains what the tool does and the return format, which is adequate for basic use. However, it lacks context on behavioral aspects like errors or dependencies, and with no output schema, it could benefit from more detail on the return structure. For a tool with no structured data, this is a bare-minimum description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and the schema description coverage is 100% (since there are no parameters to describe). In such cases, the baseline score is 4, as there are no parameters for the description to compensate for. The description does not need to add parameter semantics, and it appropriately does not include any, which is efficient and correct.
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 fetches the Qiskit v2 API reference table of contents from a specific URL, which is a specific verb ('fetch') and resource ('table of contents'). It distinguishes from some siblings like 'qiskit_v1_api_reference_toc' by specifying 'v2 (latest)', but does not explicitly differentiate from other siblings like 'fetch_as_markdown' or 'qiskit_tutorial' in terms of when to use each. The purpose is clear but sibling differentiation is incomplete.
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 when to use it over 'qiskit_v1_api_reference_toc' for older versions, or over 'fetch_as_markdown' for other markdown fetching tasks. There is no context on prerequisites, exclusions, or recommended scenarios, leaving the agent with no usage instructions beyond the basic purpose.
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?
With no annotations provided, the description carries the full burden. It discloses key behavioral traits: runs code in a temporary virtual environment, handles version-specific errors, and returns a dict with results. However, it lacks details on error handling specifics, performance implications, or rate limits, leaving some gaps in transparency for a mutation-like analysis tool.
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 appropriately sized but not optimally structured. It front-loads the purpose but includes verbose background on AI training and migration that could be condensed. Sentences like 'Errors will occur if the code uses modules or functions that are no longer supported' are useful but could be more concise. Overall, it's adequate but has some redundancy.
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 3 parameters, no annotations, and no output schema, the description is moderately complete. It explains the tool's purpose, usage, and parameters, but lacks details on the return dict structure, error specifics, or integration with siblings. For a static analysis tool with no structured support, it should provide more behavioral context to achieve higher 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?
Schema description coverage is 0%, so the description must compensate. It adds meaning beyond the schema by explaining that 'code' is AI-generated Qiskit code, 'qiskit_version' addresses migration issues from v0, and 'other_dependencies' includes packages like qiskit-ibm-runtime. This covers all parameters well, though it could provide more detail on format or constraints, hence not a 5.
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 purpose: 'Check the provided Qiskit code for static analysis.' It specifies the verb ('check') and resource ('Qiskit code'), and distinguishes it from siblings by focusing on static analysis rather than migration guides or tutorials. However, it doesn't explicitly differentiate from all siblings like 'jm_check' or 'learn_jijmodeling', which is why it's not a perfect 5.
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 clear context for when to use this tool: for checking AI-generated Qiskit code, especially due to version compatibility issues (v0 vs. v1/v2). It recommends 'Use v2 unless you have a specific reason not to' and mentions dependencies. However, it doesn't explicitly state when not to use it or name alternatives among siblings, so it falls short of a 5.
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?
With no annotations provided, the description carries the full burden. It discloses the tool's core behavior (fetching, HTML-to-Markdown conversion, error handling) but lacks details about rate limits, authentication needs, timeout behavior, or what happens with malformed URLs. The error response structure is described, which adds value.
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 well-structured with a clear purpose statement followed by Args and Returns sections. Every sentence adds value, though the Returns section could be more concise by avoiding repetition of error details.
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?
For a tool with 2 parameters, no annotations, and no output schema, the description provides adequate coverage of the core functionality and parameters. However, it lacks details about behavioral constraints (e.g., network timeouts, size limits) and doesn't fully explain the FetchResponse structure beyond success/error states.
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 must compensate. It clearly explains both parameters: 'url' as the website URL to fetch and 'headers' as optional custom request headers. This adds meaningful context beyond the bare schema, though it doesn't specify header format examples or constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('fetch', 'convert', 'return') and resource ('website HTML content to Markdown'). It distinguishes itself from sibling tools by focusing on web content conversion rather than quantum computing or modeling tools.
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 usage context (fetching and converting web content) but doesn't explicitly state when to use this tool versus alternatives. No guidance on prerequisites, limitations, or comparison with other web-fetching tools is 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool fetches content from a specific URL and returns Markdown format, which covers the basic operation and output type. However, it lacks details on error handling, rate limits, authentication needs, or whether the fetch is cached or real-time, leaving behavioral gaps for a tool that interacts with external resources.
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 and front-loaded, consisting of only two sentences that directly state what the tool does and what it returns. Every sentence earns its place by providing essential information without any redundancy or fluff, making it highly efficient for an AI agent to parse.
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 simplicity (0 parameters, no annotations, no output schema), the description is complete enough for basic understanding. It specifies the exact resource and output format. However, for a tool that fetches from an external URL, additional context on reliability, error cases, or caching behavior would enhance completeness, especially since there are no annotations to cover these aspects.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and the input schema has 100% description coverage (though empty). The description does not need to add parameter semantics, so it meets the baseline of 4 for tools with no parameters, as it appropriately focuses on the tool's purpose and output without unnecessary parameter details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Fetch') and resource ('Qiskit v1 API reference table of contents') with the exact URL provided. It distinguishes from siblings like 'qiskit_v2_api_reference_toc' by specifying the v1 version, and from 'qiskit_tutorial' by focusing on API reference documentation rather than tutorials.
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 usage context by specifying the exact URL and version (v1), suggesting it should be used for Qiskit v1 API documentation needs. However, it does not explicitly state when to use this tool versus alternatives like 'qiskit_v2_api_reference_toc' or 'fetch_as_markdown', nor does it provide any exclusion criteria or prerequisites.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes key behaviors: fetching from a specific source (IBM Quantum Learning Hub), returning error messages if the tutorial isn't found, and returning content in Markdown format. However, it doesn't cover important aspects like rate limits, authentication requirements, or whether this is a read-only operation. The description doesn't contradict any annotations since none are 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 appropriately sized and well-structured. It begins with the core purpose, then provides usage instructions, parameter details, and return information. While slightly repetitive in mentioning error behavior twice, each sentence serves a clear purpose. The information is front-loaded with the most important details first.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (single parameter, no output schema, no annotations), the description is reasonably complete. It covers purpose, usage flow, parameter semantics, error handling, and return format. The main gap is the lack of behavioral details like authentication or rate limits, but for a tutorial-fetching tool with a simple interface, this is acceptable.
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 must fully compensate. It provides excellent parameter semantics: it explains that 'tutorial_name' should be a string, specifies 'toc' as a special value for the table of contents, provides formatting requirements (lowercase with hyphens), gives concrete examples, and explains error behavior. This adds substantial meaning beyond what the bare schema provides.
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 purpose: 'Fetch a Qiskit tutorial from the IBM Quantum Learning Hub.' This specifies both the verb ('fetch') and resource ('Qiskit tutorial'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate this from sibling tools like 'fetch_as_markdown' or 'qiskit_v0tov1v2_migration_guide', which might have overlapping domains.
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 clear context on when to use this tool: it instructs to first get the table of contents and check tutorial names, and specifies that 'tutorial_name' should be 'toc' for the table of contents. This gives practical guidance on how to use the tool effectively. However, it doesn't explicitly state when to use this versus alternatives like 'fetch_as_markdown' or other Qiskit-related tools.
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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