Cloudflare Workers MCP server: api-flow-analyzer
Analyze complete API call flow and identify patterns
| Name | Required | Description | Default |
|---|---|---|---|
| calls | Yes | Array of API calls with metadata | |
| flow_id | Yes | Unique request flow identifier |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It only says 'analyze' and 'identify patterns' without stating whether the operation is read-only, what the output shape is, or whether any data is persisted. This is minimal disclosure beyond the core function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no filler, front-loading the action and resource. It is appropriately concise for the stated purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description should explain what 'patterns' means and how the analysis is delivered, but it doesn't. It also lacks usage guidance, making the tool's behavior under-specified for an agent to invoke and interpret results correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides good coverage (100%) for the two parameters, so the baseline is 3. The description adds no extra meaning to 'calls' or 'flow_id' beyond what the schema already specifies, such as expected structure or semantics of 'complete'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it analyzes the complete API call flow and identifies patterns, naming a specific verb and resource. However, it doesn't explicitly distinguish itself from sibling tools like detect_redundancy or suggest_batching, and the term 'patterns' remains somewhat vague.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 such as detect_redundancy or get_dependency_graph. No prerequisites, exclusions, or alternative scenarios are mentioned, so the agent must infer usage from the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Calculate estimated cost and latency savings from optimizations
| Name | Required | Description | Default |
|---|---|---|---|
| current_calls | Yes | Current number of API calls | |
| avg_latency_ms | No | Average latency per call in ms | |
| optimized_calls | Yes | Optimized number of calls | |
| cost_per_call_usd | No | Cost per API call in USD |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose any behavioral traits such as read-only nature, side effects, required permissions, or output format. The description only states the calculation purpose, leaving the agent without information about what the tool does beyond the basic function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence with no unnecessary words. It is appropriately sized for a simple calculation tool and gets straight to the point.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a straightforward calculation tool with no output schema, the description covers the core purpose. However, it omits details like the output format, assumptions (e.g., default cost per call), and any edge cases. It is minimally complete but not rich.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides full descriptions for all four parameters (100% coverage), so the baseline is 3. The description adds no additional parameter semantics beyond what the schema already offers, but it doesn't need to compensate since coverage is complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool calculates estimated cost and latency savings from optimizations, which is a specific verb and resource. It clearly differentiates from sibling tools like analyze_flow or detect_redundancy by focusing on savings calculation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The usage is implied by the context of the sibling tools (e.g., after detecting redundancy or suggesting batching), but there is no explicit guidance on when to use this tool versus alternatives. It lacks a clear when-to-use or when-not-to-use statement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Identify duplicate or redundant API calls in a flow
| Name | Required | Description | Default |
|---|---|---|---|
| calls | Yes | Array of API calls to analyze | |
| threshold_pct | No | Similarity threshold percentage (0-100) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must fully disclose behavioral traits. It only states the action ('identify') without explaining how redundancy is determined, whether the tool is read-only, what the threshold_pct does, or what output format to expect. This is a significant gap for an 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is direct, front-loaded, and free of any fluff or redundant wording. It conveys the core idea efficiently without wasting the agent's attention.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema and a nested array parameter, so the description should explain what the tool returns and how the analysis works. It only covers the action, not the results, threshold behavior, or processing details, leaving a notable gap for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides descriptions for both parameters ('calls' and 'threshold_pct'), covering 100% of the schema. The description adds no additional parameter context, so it neither improves nor detracts from the schema's clarity, meeting the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: identify duplicate or redundant API calls in a flow. The verb 'identify' is specific and the resource is well-defined. It is distinct from siblings like analyze_flow or suggest_batching, though it does not explicitly contrast them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool (when looking for redundant API calls) but provides no explicit guidance on choosing it over alternatives like analyze_flow or suggest_batching. There are no 'use when' or 'instead of' statements, so the usage context remains implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate dependency graph of API calls showing call sequence and relationships
| Name | Required | Description | Default |
|---|---|---|---|
| calls | Yes | Array of API calls with dependencies |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It mentions 'showing call sequence and relationships' but does not disclose side effects, return format, assumptions, or limitations. It is a minimal description with no contradiction to annotations (since none exist).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that is clear and efficient. It contains no wasted words and immediately communicates the tool's function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With one parameter and no output schema, the description is functional but lacks context on expected output format or use cases. It is adequate for a simple tool but does not fully guide an agent on what to expect or when to invoke it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema fully describes the only parameter 'calls' as 'Array of API calls with dependencies', so the baseline is 3. The description does not add any extra parameter-level detail beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Generate' and resource 'dependency graph of API calls', clearly stating what it produces. It distinguishes itself from sibling tools like calculate_savings or track_call by focusing on graph generation, though it doesn't explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 vs analyze_flow or detect_redundancy. The description only states what the tool does, not the context or scenarios where it is preferred.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recommend batching opportunities for parallel API calls
| Name | Required | Description | Default |
|---|---|---|---|
| calls | Yes | Array of API calls to optimize | |
| batch_size_limit | No | Max items per batch |
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 only says 'recommend' (implying no mutation but not explicitly), and it does not disclose what the output looks like, whether the tool is read-only, or any relevant limitations. Lack of behavioral detail 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, punchy sentence that immediately communicates the core function. There is no filler or repetition, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Without an output schema and with no annotations, the description must explain what the tool returns or how it behaves. It only states the high-level purpose, leaving the agent uninformed about the result structure, possible side effects, and edge cases. The schema covers parameters but not the overall tool behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already provides 100% coverage with descriptions for both parameters ('Array of API calls to optimize' and 'Max items per batch'). The description adds no parameter-specific meaning beyond this, so a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('recommend') and a clear resource ('batching opportunities for parallel API calls'), which exactly conveys the tool's function. It also distinguishes this tool from siblings like analyze_flow or calculate_savings by focusing on batching opportunities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage: when you need batching opportunities for parallel API calls. However, it provides no explicit guidance on when not to use it or how it compares to sibling tools, so there is room for clearer alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Record and track an individual API call with metadata
| Name | Required | Description | Default |
|---|---|---|---|
| method | Yes | HTTP method (GET, POST, etc.) | |
| endpoint | Yes | API endpoint URL | |
| duration_ms | Yes | Call duration in milliseconds | |
| status_code | Yes | HTTP status code | |
| service_name | No | Source service identifier | |
| response_size_bytes | No | Response payload size |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations available, the description carries the full burden of behavioral disclosure. It only hints at a write/recording operation but does not explain side effects, persistence, authentication, or return values. This is insufficient for an agent to predict the tool's impact.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, direct sentence with no filler. However, it is under-specified, which detracts from its structure since it omits valuable context; for a tool with 6 parameters, a slightly more detailed description would be appropriate.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
There is no output schema and no annotations, yet the tool has six parameters. The description is extremely brief and does not explain return values, the relationship to siblings, or any behavioral nuances. This leaves the tool largely underdocumented.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides complete descriptions for all six parameters, so the baseline is 3. The description's mention of 'metadata' adds no specific meaning beyond the schema, so no additional credit is warranted.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool records and tracks an individual API call with metadata, using specific verbs and a clear resource. This distinguishes it from the sibling analysis tools (analyze_flow, calculate_savings, etc.) which operate on flows or multiple calls.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 the sibling tools. The description does not mention any preconditions, alternatives, or exclusions, leaving the agent to infer usage from purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
analyze_flowcalculate_savingsdetect_redundancyget_dependency_graphsuggest_batchingtrack_callClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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Each tool targets a distinct aspect of API flow analysis: overall flow, savings, redundancy, dependency graph, batching, and individual call tracking. No two tools have overlapping purposes.
All tool names follow a consistent verb_noun pattern using snake_case, making the set predictable and easy to navigate.
Six tools is well-scoped for an API flow analyzer, covering core diagnostics without unnecessary bloat or missing essentials.
The surface covers tracking, analysis, redundancy detection, batching, dependency graphing, and savings calculation. Minor gaps like cleanup or export are absent but not critical for the domain.