durable-mcp

by cshah26

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Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Tools target distinct domains (constraints, generation, spreads, PJM) with clear descriptions. Minor overlap exists between get_spread_analysis and get_spread_portfolios, but their granularity differs (path/book vs portfolio summary).
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern: 'get_' for data retrieval and 'list_' for enumeration. No mixed conventions or vague verbs.
Tool Count5/5
11 tools cover the energy trading analytics domain comprehensively without being excessive. Each tool serves a clear purpose and earns its place.
Completeness4/5
The tool set covers listing, detail, and comparison for constraints, generation, spreads, and prices. A minor gap is the lack of tools for submitting trades or updating data, but the server appears focused on read-only analytics.
Average 4/5 across 11 of 11 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 8 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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This repository includes a README.md file.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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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?
The description details the returned data (hourly spread, summaries, peak-hour breakdown) but does not disclose behavioral aspects like data freshness, rate limits, or authorization needs. With no annotations, the description could add more context.
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 two sentences, front-loaded with purpose, and includes usage guidance. Every sentence adds value without 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?
For a data retrieval tool with 4 parameters, no output schema, and no annotations, the description adequately explains what it returns and when to use it. Additional detail on return format would improve completeness.
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?
Input schema has 100% parameter descriptions. The tool description does not add extra semantic meaning beyond the schema, so baseline score of 3 applies.
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 retrieves DA and RT LMP price spreads between two nodes over a date range and lists output types. It does not explicitly differentiate from sibling tools like get_spread_analysis but provides use-case examples.
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?
It explicitly says 'Use this when asked about spread between locations...' providing clear context for invocation. However, it does not mention when not to use this tool or suggest alternative siblings.
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 bears full burden. It does not disclose read-only nature, idempotency, rate limits, auth requirements, data freshness, or any side effects. This is a significant gap for a 'get' operation that should be safe.
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 on purpose, second on usage. No extraneous information, front-loaded, and each sentence serves a clear function.
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?
With no output schema, the description gives a reasonable sense of what is returned (prices and load). Combined with fully described parameters, it is adequate for simple data retrieval. However, it lacks details on response structure or pagination if any.
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 the schema already documents both parameters well. The description adds context about the returned data (prices and load) but does not elaborate further on parameter format or usage beyond 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 retrieves PJM Western Hub DA/RT LMP prices and load data, with specific location and data types. It distinguishes well from sibling tools that handle constraints, flows, generation, etc.
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 lists use cases ('PJM Western Hub prices, PJM WH, Eastern power prices, or PJM load'), but does not mention when not to use or reference sibling alternatives directly. However, the given contexts are clear for an agent.
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, and the description does not disclose behavioral traits such as read-only status, authentication requirements, or potential side effects. The tool likely performs a read operation but this is not confirmed.
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 with two sentences, front-loading the core function and output, followed by usage triggers. Every sentence adds value with 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 no output schema, the description covers return fields (total PnL, win rate, best/worst days, per-portfolio breakdown) and parameter range (date range). It is mostly complete but could mention error handling or data freshness.
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 already documents all parameters adequately. The description adds no additional parameter details beyond what is in the schema, achieving the baseline score.
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 retrieves a PnL summary for spread trading portfolios, listing specific return fields (total PnL, win rate, etc.) and distinguishes from siblings by focusing on portfolio performance rather than other analyses.
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 advises when to use the tool—'when asked about portfolio performance, PnL, profit/loss, trading results'—but does not mention when not to use it or provide 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 exist, so description must carry full burden. It explains the calculation and data sources but omits behavioral traits like read-only nature, data freshness, or pagination. Adequate but not exhaustive.
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: first defines purpose, second adds detail on meaning and usage. No fluff, every sentence adds value.
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?
With no output schema, the description lacks detail on return format. It only mentions 'shows average percentage difference'. For a complex tool, more context on output structure would help.
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. Description adds example fuel type values and notes partial match for plant_name, but does not significantly enhance understanding beyond 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 compares generator forecast (MUSE) vs actual (Edison) output, specifies the metric (average percentage difference), and explains directionality. This is specific and distinct from siblings like get_gen_detail.
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 when to use: 'when asked about generator forecasts, forecast accuracy, MUSE vs Edison, plant output, or fuel-type generation.' Lacks explicit when-not or alternatives, but the context is clear.
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 the description carries the full burden. It explains what the tool returns and that high values indicate near limit, but does not disclose read-only nature, authentication needs, rate limits, or any side effects. Adequate but not detailed.
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: first describes the function, second gives usage context and interpretation. 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 only 3 parameters, no output schema, and no annotations, the description covers the essential aspects: what it returns, usage context, and value interpretation. Could mention output format but is sufficient for the tool's simplicity.
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 description need not add parameter details. It does not provide additional meaning beyond the schema's own descriptions (dates format, constraint name lookup). 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 retrieves hourly max and min loading percentages for a specific ERCOT transmission constraint over a date range. It distinguishes from siblings like get_flow_analysis by focusing on 'constraint loading' and transmission utilization.
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 says 'Use this when asked about constraint loading, line loading, transmission utilization, or how loaded a specific constraint is.' Provides clear context but does not mention when not to use it or alternative tools.
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 carries full burden. It explains output (most loaded/at risk constraints) but omits behavioral traits like read-only guarantee, pagination, or auth requirements. Adequate for a simple read 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?
Three concise sentences: function, outcome, usage context. No extraneous words, well 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 simplicity (one parameter, no output schema, no annotations), the description sufficiently covers purpose, usage, and parameter format. Minor gap: no mention of return value structure.
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% for the single parameter 'date' with a clear description. The tool description does not add extra meaning beyond the schema, meeting the baseline.
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 'Get' and resource 'all ERCOT transmission constraint loading for a single date.' It clearly distinguishes from sibling tools like 'get_constraint_loading' by specifying scope and risk assessment.
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 when to use: 'when asked about transmission congestion, binding constraints, or overall grid loading on a specific day.' However, lacks explicit when-not-to-use or alternative tool references.
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 carries the full burden. It states the tool returns counts per day and portfolio, implying read-only behavior, but does not explicitly declare it as read-only, mention any destructive potential, or detail side effects. Basic transparency but no additional context.
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 two sentences long, with the core purpose in the first sentence and usage guidance in the second. No fluff, every sentence adds value.
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 no output schema and 3 parameters, the description adequately explains the output concept (counts per day and portfolio) and usage. It could include details on response format or aggregation, but it is sufficient for an agent to understand what the tool returns.
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 the input schema already describes all three parameters. The description adds a high-level context (counts per day and portfolio) but does not provide additional parameter-level meaning beyond what the schema's descriptions already offer. 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 returns bid segment counts per day and portfolio, with a specific verb ('Get') and resource ('segment counts'). It distinguishes from siblings by focusing on segment counts and submission status, which is unique among the listed siblings like get_spread_analysis or get_nodal_spread.
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 when to use it: 'Use this when asked about bid activity, segment counts, submission status, or how many bids were placed.' This provides clear context but does not specify when not to use it or mention 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?
With no annotations, the description carries full burden. It implies read-only ('Get detailed breakdown') and mentions specific metrics (win rate, Calmar ratio), but does not disclose potential side effects, auth needs, or rate limits. Adequate but could be more comprehensive.
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: first states purpose and outputs, second gives usage guidance. Very concise, front-loaded, and 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?
For a tool with 4 params, no output schema, and no annotations, the description covers purpose, usage, and key metrics shown. It lacks output format details but compensates by listing metrics. Slightly incomplete regarding error conditions or permissions, but sufficient given 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 coverage is 100%, so baseline is 3. The description reinforces breakdown options by explaining 'path (source→sink)', but adds little beyond schema descriptions for other parameters. It does not add new parameter-specific meaning.
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 starts with 'Get detailed breakdown of spread trading PnL by path, book, or book+path', providing a specific verb and resource. It clearly distinguishes from sibling tools like get_flow_analysis by focusing on spread trading PnL breakdowns.
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 this when asked about specific paths, books, source/sink pairs, or route-level performance', providing clear context for when to use. While it doesn't explicitly list when not to use, the sibling list implies 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 exist, but the description implies a read-only search operation. It doesn't disclose any behavioral traits like pagination, rate limits, or prerequisites. Adequate but lacks depth for a tool with no annotations.
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 states purpose, second gives usage context and return value. Every word serves a purpose, 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?
For a simple search tool with 2 parameters and no output schema, the description adequately explains the use case and output fields. Could mention pagination or ordering, but it's not severely lacking given the tool's simplicity.
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% with clear parameter descriptions. The tool description adds no new parameter information beyond what the schema already provides, so the baseline 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?
Clearly states it searches for price nodes by name, and explicitly connects it to a downstream tool (get_nodal_spread). Also lists the returned fields, making the tool's role unambiguous.
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 advises using this tool before get_nodal_spread, providing a clear workflow. No mention of when not to use it or comparison with sibling list_constraints, but the guidance is specific and helpful.
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 indicates a read operation ('Get') and the data type (hourly comparison), but does not explicitly state read-only status, permissions, or potential side effects. The description adds some context but not full 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 consists of two concise sentences: the first states the core function, and the second provides usage guidance. No extraneous information, efficiently packed.
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 3 required params and no output schema, the description covers purpose and usage but lacks details on output structure (fields expected) or limitations. It is minimally adequate but not fully complete.
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%, with each parameter described in the schema. The description does not add further parameter-specific meaning beyond what the schema already provides. 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 returns hourly Edison vs MUSE generation detail for a specific plant, with explicit use cases (hourly output, forecast vs actual, time series). This distinguishes it from sibling tools like get_gen_comparison, which is referenced for finding plant names.
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?
The description explicitly states when to use the tool: when asked about a specific plant's hourly output, forecast vs actual, or time series. It also indirectly advises against using it for finding plant names by referencing get_gen_comparison.
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, and description only states it lists constraint names. It does not disclose any behavioral traits such as read-only nature, performance, or data freshness. For a simple list operation, the lack of detail is acceptable but not exceptional.
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, no fluff, front-loaded with purpose. Every sentence is meaningful.
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 zero parameters and no output schema, the description is nearly complete. It could briefly mention that output is a list of constraint names, but the context of being used before get_constraint_loading implies the return format. Minor gap: no explicit output 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?
Input schema has zero parameters (100% coverage). According to guidelines, baseline is 4 for 0 parameters. Description adds no parameter info as none exist, which 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?
Description clearly states verb 'List', resource 'ERCOT transmission constraint names', and purpose 'to find constraint names before calling get_constraint_loading'. It distinguishes from sibling tool get_constraint_loading by indicating this is a preparatory step.
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 tells when to use this tool: 'Use this to find constraint names before calling get_constraint_loading.' This provides clear context and alternative/next-step tool reference.
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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