logicstamp-mcp

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It details execution command, returned data (token counts, savings, file stats), explains mode savings percentages, and notes performance impact (2-3x longer). Fully transparent about behavior and 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is two sentences, front-loaded with purpose. Second sentence packs many details but remains readable. Slightly verbose but efficient overall.

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?

Given schema covers all params, no output schema, and moderate complexity, the description fully explains purpose, modes, usage, performance, and expected output. No gaps remain.

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?

Schema coverage is 100% with both parameters described. Description does not add any extra meaning about parameters beyond the schema, so baseline 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?

Description states it 'generates token cost comparison across all modes' to help choose optimal mode. The verb and resource are specific. Siblings like logicstamp_compare_snapshot are different, so this tool is clearly distinguished.

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?

Description explicitly says 'Use before generating context, when user asks about token budgets, or to evaluate style metadata impact.' Provides clear when-to-use context, though doesn't explicitly list when not to use or name alternative tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description fully covers behavioral traits: it reads context_main.json and folder context.json, returns a structured diff with token deltas, and details parameter effects (e.g., forceRegenerate triggering regeneration, baseline options). It also describes error conditions when files are missing.

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 packed with information but remains concise and well-structured. It begins with a clear one-line purpose, then logically flows into parameter details and use cases, avoiding unnecessary elaboration.

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?

Given the tool has 8 parameters, no output schema, and no annotations, the description is complete. It covers return type (structured diff with token deltas), error handling (missing context_main.json), and all parameter interactions, providing a fully sufficient guide for the agent.

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?

All 8 parameters are described in the schema (100% coverage), and the description adds significant context beyond the schema—e.g., explaining default behaviors, examples for depth, and the effect of baseline values. This extra explanation enhances meaning without redundancy.

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 'Compares current snapshot with baseline to detect changes' and specifies the types of changes detected (ADDED/REMOVED/CHANGED/UNCHANGED). It uses concrete verbs and resources, and distinguishes itself from sibling tools like logicstamp_refresh_snapshot by noting that it reads from disk or regenerates.

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?

Explicitly states 'Use after editing files to verify changes' and provides a comparison to Jest snapshots. It also advises when not to use it: if context_main.json is missing and forceRegenerate is false, the agent should first run refresh_snapshot or set forceRegenerate=true.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so the description must disclose behavior. It explains that bundles are fresh in watch mode, that it lists only root bundles (not dependencies), and that it handles race conditions internally. Lacks an explicit statement that it is read-only, but the context 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with a critical warning and structured with bullet points. While somewhat verbose, every sentence adds essential information. Could be slightly trimmed but overall effective.

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?

Given no output schema, the description explains what the tool returns (bundle catalog with fields). It also provides context about dependencies, next steps, and all three parameters, making it complete for the tool's complexity.

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?

Schema coverage is 100%, so baseline 3. The description adds value by explaining when to use each parameter (e.g., snapshotId vs projectPath based on watch mode) and provides an example for folderPrefix, going beyond the schema definitions.

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 lists all ROOT bundles from context_main.json, returns a bundle catalog with specific fields, and distinguishes between root components and dependencies. It also explains what to do if a component is missing, making the purpose unmistakable.

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?

Provides explicit critical guidance: not to use sleep() delays, when to use projectPath vs snapshotId, how to filter with folderPrefix, and what tool to call next (read_bundle). This differentiates it from siblings like logicstamp_read_bundle.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden and delivers: it discloses internal retry logic, race condition handling, and the pre-parsed nature of the data. It clearly indicates a read-only operation without mutating state, meeting transparency needs.

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?

While lengthy, the description is well-structured with a critical warning upfront, followed by operational details. Every sentence adds unique value, though it could be slightly condensed without losing substance.

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?

Given 4 parameters, no output schema, and moderate complexity, the description covers all essential aspects: usage scenarios, parameter relationships, output structure details (graph, edges, meta, UIFContract), and special behaviors. It is fully actionable for an AI agent.

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?

Schema coverage is 100%, providing baseline of 3. Description adds significant value by explaining the interplay between snapshotId and projectPath, how bundlePath relates to context files, and the rootComponent constraint. This goes well beyond the schema descriptions.

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 reads bundle/index files for component contracts and dependency graphs, and differentiates between root and dependency components. Although it doesn't explicitly contrast with sibling tools, the purpose is specific and well-defined.

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?

Excellent guidance: explicitly warns against using sleep delays, explains when to use snapshotId vs projectPath, distinguishes root vs dependency lookup, and describes internal retry logic. This fully informs the agent about when and how to use the tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden. It transparently describes the tool's output (documentation bundle) and content areas (what LogicStamp is, workflow, best practices). It does not mention side effects or authorization, but as a read-only documentation tool, this is acceptable.

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 appropriately sized and front-loaded with the main purpose. However, it repeats some information (e.g., 'Explains' section overlaps with earlier list) and could be slightly more concise without losing clarity.

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?

Given zero parameters and no output schema, the description fully covers what the tool does, when to use it, and what it returns. It addresses all likely information needs for an agent selecting this tool.

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 has zero parameters, so the description only needs to explain the tool's function. It does so comprehensively, listing the documentation contents and use cases, adding significant meaning beyond the empty schema.

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 returns comprehensive LogicStamp documentation, listing specific contents (guide, usage, contracts, schema, CLI, limitations). It distinguishes from sibling tools that focus on comparing modes, snapshots, or reading specific bundles, establishing unique value.

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 explicitly states when to use the tool ('unsure how LogicStamp works, starting new project, need bundle structure/contract format') and includes an 'escape hatch' for confusion. While it provides clear context, it does not explicitly state when not to use it or alternatives, though sibling differentiation is implicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden. It discloses that the tool is slow, generates structured data not raw source, and that the summary lacks component details. It also warns about the critical `projectPath` requirement and that omitting it causes hangs. However, it doesn't explicitly mention potential side effects like file overwrites, though it implies safety through auto-detection and cache cleanup options.

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 long but well-structured with emojis, bold text, and clear sections. It front-loads the critical warning. Some redundancy exists (e.g., multiple 'CRITICAL' markers), but every sentence adds value. It could be slightly more concise without losing information.

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?

Given 7 parameters, 1 required, and no output schema, the description covers everything adequately. It explains the tool's output (summary statistics, snapshotId), what it doesn't include (component details), and the next steps (list_bundles, read_bundle). It also addresses style metadata and depth parameter effects. For a complex tool, this is very complete.

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?

Schema description coverage is 100%, but the description adds substantial value beyond the schema. It explains the workflow, provides examples for depth, warns about projectPath being required despite being in the schema, and clarifies the skipIfWatchActive behavior. This exceeds the baseline expectation for a fully covered schema.

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 purpose: it runs `stamp context` to analyze React/TypeScript/Node.js codebases and generate structured context files. It distinguishes itself from siblings by specifying when to use it versus `logicstamp_watch_status` and when to skip and use `list_bundles`/`read_bundle` directly.

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 explicit when-to-use (watch mode inactive, first-time analysis, after large changes) and when-not-to-use (watch mode active, skip and read bundles directly). It instructs to first call `logicstamp_watch_status` and names alternatives like `list_bundles` and `read_bundle`. The default `skipIfWatchActive=true` is explained.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden and excels. It discloses internal behavior: reads .logicstamp/context_watch-status.json, verifies process running, describes watch features (incremental rebuilds, change detection, debouncing, optional log file), strict watch mode detection, and that tools handle race conditions internally.

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 front-loaded with a critical warning and structured with sections, but it is verbose and repetitive (e.g., multiple mentions of not using sleep). Some sentences could be consolidated without losing meaning.

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?

Given no output schema, the description fully explains what the tool returns (status info). It covers workflow integration, prerequisites, edge cases (strict watch, log file requirement), and internal behavior, making it complete for the tool's role in a set of sibling tools.

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?

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the contextual use of includeRecentLogs (to see recent regeneration events) and logLimit (maximum entries), and emphasizes the requirement for projectPath. This extra context justifies a score above baseline.

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 checks if watch mode is active, and provides explicit instructions on workflow integration with sibling tools like refresh_snapshot, list_bundles, and read_bundle. The purpose is unambiguous and specific.

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 explicitly states to call this first before any other LogicStamp tool, and provides detailed conditional guidance: if active, skip refresh_snapshot; if inactive, call it. It also warns against using sleep() delays, offering clear when-to-use and when-not-to-use instructions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.