alterlab-mcp-server

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

Without annotations, the description carries full burden. It discloses parallel execution, anti-bot bypass, immediate batch_id return, and credit pre-debit/refund. Missing details on error handling or rate limits, but overall informative.

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?

Five sentences, each adding value: core function, benefit, immediate response, customization, credit behavior. No redundancy, well-structured, and front-loaded.

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?

Covers the main workflow and key behaviors. Lacks mention of expected result format from 'alterlab_batch_status', but since that's a separate tool, completeness is acceptable.

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 context about per-URL customization and anti-bot protection but does not significantly extend beyond 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 the tool scrapes up to 100 URLs in parallel, using a specific verb ('scrape') and resource ('URLs'). It distinguishes itself from sibling 'alterlab_scrape' by emphasizing efficiency and parallelism.

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?

Explicit guidance is provided: 'Use this whenever you need to scrape more than 2-3 URLs.' This advises when to use the batch tool versus sequential alternatives, fulfilling the dimension well.

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 explains behavior: concurrent scraping, per-URL status, content preview, cost summary, and graceful handling of partial failures.

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?

Concise yet comprehensive; front-loaded with core purpose, then contrasts with sibling, usage guidelines, and behavioral insights—every sentence is informative.

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 6-parameter tool with no output schema or annotations, the description covers purpose, usage, behavior, and results sufficiently, making it easy for an agent to use 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?

Schema provides full parameter descriptions (100% coverage), but the description adds value by explaining the use case and concurrency behavior, integrating parameter choices into overall context.

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 scrapes multiple URLs simultaneously and returns results synchronously, distinguishing it from alterlab_batch which uses async polling.

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 recommends using this tool for 2-20 URLs when immediate results are needed, and directs users to alterlab_batch for 20+ URLs, providing clear when-to-use and when-not-to-use guidance.

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?

Discloses return behavior with status values and when results are returned. No annotations are provided, so the description carries the burden; it covers the key behaviors for a polling tool, though it doesn't explicitly state non-destructiveness.

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?

Three short sentences that are well-structured, front-loading the purpose and then providing status values and results. No redundancy.

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?

Adequately covers usage, status values, and result content. No output schema, but the description explains what to expect. For a simple polling tool with one parameter, this is sufficiently 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?

Only one parameter (batch_id) with 100% schema description coverage. The tool description reinforces that the batch_id comes from alterlab_batch, but adds no new semantic 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 clearly states the action (poll) and resource (status and results of a submitted batch), and it distinguishes from siblings by specifying that this is to be used after alterlab_batch.

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 says to call after alterlab_batch, providing clear usage context. Does not mention when not to use or alternatives, but the context is clear enough.

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?

Describes exactly what is returned (balance, status, deposited, spent) and notes that no parameters are required, using API key for identification. No annotations exist, so description fully covers behavioral traits.

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?

Two sentences with no wasted words, front-loaded with purpose, and includes a tip and an alternative reference.

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 return values and provides context about when to use and alternative tools, 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?

No parameters in schema, and the description explicitly states 'No parameters required — uses your API key for identification,' adding value 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 checks account balance, status, and credit usage. It distinguishes from sibling alterlab_get_usage by noting that tool provides a full spending breakdown.

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 advises calling before expensive operations like batch scrapes or large crawls. Also provides an alternative tool (alterlab_get_usage) for detailed breakdown, giving both when-to-use and when-not-to-use guidance.

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. It discloses key behaviors: auto-bypassing anti-bot protection (works on Cloudflare), URL discovery via sitemap and link extraction, asynchronous scraping, return of crawl_id for polling, and support for patterns, render_js options, and cost controls. Missing details on rate limits or error handling, but overall transparent.

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 for a complex tool, with each sentence adding meaningful information. It front-loads the main purpose and then lists features. Could be slightly improved with bullet points or clearer grouping, but it is well-structured and not verbose.

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 22 parameters, high schema coverage, and no output schema, the description is comprehensive. It covers major use cases, parameter interactions, and return behavior (crawl_id for polling). Minor gap: does not describe output format details beyond 'clean markdown, text, or structured data,' but sufficient for initial selection.

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 beyond schema by explaining render_js='auto' savings, cost_controls for capping or pinning tiers, sitemap_path for non-standard locations, and headers for injection. This enriches understanding and usage.

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: 'Crawl an entire website for RAG, index all pages on a site, or build a knowledge base from a domain.' It uses specific verbs and resources, and distinguishes from siblings like alterlab_scrape (single page) by emphasizing full-site crawling.

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 guidance on when to use the tool (for site crawling, RAG, knowledge base), mentions polling with alterlab_crawl_status, and explains scoping patterns and cost controls. It implies alternatives (e.g., single scrapes via alterlab_scrape) but does not explicitly state when not to use or compare to siblings.

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, description carries full burden. It discloses key behaviors: cancellation stops crawling, refunds credits, keeps already-scraped pages (via alterlab_crawl_status), and prohibits resumption. Sufficiently transparent for a simple cancellation tool, though auth or rate limits are not mentioned.

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?

Two sentences, zero waste. First sentence captures primary action and refund. Second sentence adds important details about persistence and non-resumability. Efficient and front-loaded.

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 simple tool with one parameter and no output schema or annotations, the description fully covers what the tool does, what happens to scraped pages, and what cannot be done afterwards. No significant gaps.

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?

Only one parameter 'crawl_id' with schema coverage 100%. Description adds no extra meaning beyond the schema's 'Crawl ID to cancel'. Baseline 3 is appropriate as schema already documents the parameter.

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 clearly states verb 'cancel', resource 'ongoing crawl', and additional action 'refund unused pre-debited credits'. Distinguishes from sibling tools like alterlab_crawl and alterlab_crawl_status by specifying cancellation and refund behavior.

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 implies when to use (to stop a crawl) and notes that cancelled crawls cannot be resumed, but does not explicitly state when not to use or mention alternative tools. Provides context about already-scraped pages but lacks exclusions.

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 description carries full burden. It explains polling behavior and that results are present when completed, but does not specify idempotency, whether results persist after first read, or any side effects. Adequate but not deep.

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?

Two sentences with no wasted words. Front-loaded with purpose, then adds details on status values and output. Excellent conciseness.

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?

No output schema, so description explains that 'results contains the scraped page content' when completed. Lists status values. Could mention if results are paginated or size limits, but complete enough for a simple status poll 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?

Schema coverage is 100%, so baseline 3. Description adds context by noting crawl_id is returned by alterlab_crawl, helping the agent understand the parameter source. Slight improvement over bare 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?

Description clearly states it polls status and results of a crawl, explicitly naming the action and resource. It distinguishes itself from siblings like alterlab_crawl and alterlab_crawl_cancel by specifying it is the follow-up call for progress checking.

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 says 'Call this after alterlab_crawl' and lists status values, providing clear context. Does not include explicit when-not-to-use or alternative tools, but given sibling set, this is the only status poll for crawls.

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 handles the burden. It mentions secure storage and reusability, but lacks details on conflict handling, idempotency, or session expiration.

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 concise (three sentences) and front-loaded with the main action. It efficiently communicates the tool's purpose and usage.

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 description covers the main use case and what the session enables, but does not explicitly state the return value (likely session_id) or address potential conflicts when creating a session with an existing name.

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%, so baseline is 3. The description does not add extra meaning beyond what the schema provides for individual parameters.

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 action ('Create'), the resource ('a new stored session'), and the purpose ('for authenticated scraping'), distinguishing it from sibling tools like delete_session, list_sessions, etc.

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: create a session when you need to scrape behind login walls. However, it does not explicitly state when not to use it or compare with alternatives like refresh_session or validate_session.

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?

Since no annotations are provided, the description carries full weight. It discloses that the deletion is permanent and removes cookies, which are critical behavioral traits. No contradictory or missing safety cues.

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 two sentences long, front-loaded with the main action, and contains no superfluous words. Every sentence adds value: purpose and usage context.

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 simple tool with one parameter, no output schema, and no annotations, the description is complete. It covers action, effect, and appropriate use case, leaving no obvious gaps.

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 already fully describes the single parameter (session_id as a UUID). The description does not add new semantic information beyond the schema; it simply references the session. With 100% schema coverage, the baseline score 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 explicitly states 'Delete a stored session' and emphasizes permanence, distinguishing it from sibling session tools like create, get, update, or refresh. The verb 'delete' and resource 'session' are specific and unambiguous.

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 clear when-to-use guidance: 'Use this when a session is no longer needed or has been compromised.' This is explicit context, though it does not formally exclude alternatives. However, the purpose is sufficiently distinct from siblings.

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 the full burden. It discloses idempotency ('calling it when not opted in returns success') and the limitation on GA features. It does not mention side effects, auth needs, or error behavior, but the operation is simple enough that this is adequate.

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 three sentences, front-loading the purpose, then adding idempotency and constraint. No wasted words.

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 simple tool with one parameter and no output schema, the description covers purpose, idempotency, and a constraint. It also references a sibling tool for listing slugs. Minor gaps: no mention of return values or error states, but overall adequate.

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% since the parameter 'slug' already has a description. The tool description adds no new semantic detail beyond the schema, so baseline 3 applies.

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: 'Opt out of a beta feature on your AlterLab account.' It uses a specific verb-resource pair and distinguishes from sibling tools like 'alterlab_enable_beta_feature' by noting that GA features cannot be disabled.

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 context by mentioning idempotency and the constraint that GA features cannot be disabled. It also directs users to 'alterlab_list_my_beta_features' to find valid slugs. However, it does not explicitly contrast with enabling or list all alternatives.

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 mentions idempotency and experimental nature, but does not disclose potential side effects, permissions required, or reversibility (e.g., via 'alterlab_disable_beta_feature'). More behavioral context would be beneficial.

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 consists of two concise sentences with no wasted words. It front-loads the action and efficiently adds necessary context.

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 single-parameter tool with no output schema, the description adequately covers purpose, parameter guidance, and a related sibling. It could mention the response format, but it's not essential.

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% and the description adds value by providing an example slug format and referencing the list endpoint for discovery, going beyond the schema's minimal description.

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 action ('Opt in to a beta feature'), the resource ('AlterLab account'), and distinguishes it from siblings like 'disable_beta_feature' and 'list_beta_features'.

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 advises to use 'alterlab_list_beta_features' to discover available slugs, guiding when to use the tool. It does not explicitly state when not to use it, but the context is sufficient for a simple opt-in 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?

Description covers key behavioral facts: no credits charged, returns tier/cost/confidence, explains tier pricing, and honestly notes that the 'formats' parameter doesn't affect cost estimate. With no annotations, this fully compensates.

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?

Five concise sentences: purpose, returns, usage, tier breakdown, flag behavior. No fluff, each sentence serves a purpose. Front-loaded with core 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?

Covers all necessary aspects for a cost estimation tool: purpose, return values, pricing tiers, parameter effects, and usage advice. No output schema, but return values are well described. 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?

Adds significant value beyond schema: explains render_js forces Tier 4 minimum, formats don't affect cost, use_proxy adds +1 credit, and describes format options in detail. Schema coverage is 100%, but description enriches each parameter.

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 clearly states the tool's purpose: preview scraping cost before committing, with no credit charge. It distinguishes itself from siblings (e.g., alterlab_scrape, alterlab_crawl) by focusing on cost estimation rather than actual scraping.

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 advises to call this before scraping protected or unfamiliar sites to avoid surprises, and shows how to use flags (render_js, use_proxy) to explore cost implications. This is strong usage guidance.

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 are provided, so the description carries full disclosure burden. It discloses that the tool operates on raw HTML/text/markdown, does not fetch URLs, returns JSON, explains cache behavior (including 'only' mode never calling LLM), and mentions charging for extraction_prompt. However, it lacks details on authentication requirements, rate limits, or idempotency, which would elevate it to 5.

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 detailed and informative, with the core purpose front-loaded. Every sentence adds value, but it could be slightly more structured (e.g., separating profile list from other details). Minor redundancy in explaining extraction_template as an alias for extraction_profile. Still, it is concise given the complexity of 13 parameters.

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 13 parameters, nested objects, and no output schema. The description covers the main functionality, clearly differentiates from siblings, explains key parameters, and gives usage hints. However, it could elaborate more on the output structure (especially since no output schema) and potential edge cases like error handling. Despite this, it is fairly complete for the 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%, but the description adds significant value beyond the schema. It explains the mutual exclusivity of extraction_profile and extraction_template, notes that extraction_prompt incurs extra charges, elaborates on cache options, and provides context for profiles. This helps the agent understand parameter semantics beyond simple 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 the tool's purpose: 'Extract product data, scrape prices, get structured data from any page content, or pull specific fields like names, emails, and ratings from HTML.' It distinguishes itself from sibling alterlab_scrape by explicitly stating it does NOT scrape a URL. The list of profiles and mention of extraction_prompt add further specificity.

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 guidance on when to use this tool vs alternatives: 'For scraping + extraction in one step, use alterlab_scrape with extraction_schema instead.' It also advises using extraction_prompt for natural language extraction and cache='only' for retrieving cached results. This ensures the agent knows the appropriate contexts.

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 description carries full burden. It describes the data returned (status, cookie names, usage stats, expiry, notes) and implies a read operation. It does not mention side effects, auth, or rate limits, but for a simple get operation this is adequate.

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?

Two sentences: first states function and return, second gives usage guidance. 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.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given one required parameter, full schema coverage, no output schema, and clear sibling context (CRUD for sessions), the description fully covers what an agent needs to know to use this tool 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?

Only one parameter (session_id) with 100% schema coverage (UUID format). The description adds context for why the parameter is needed ('inspect a session') beyond the schema, providing extra value.

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 'Get detailed information about a specific stored session' and lists specific return fields (status, cookie names, usage statistics, expiry info, notes). It distinguishes from sibling tools by saying 'inspect a session before deciding to validate, refresh, or delete it.'

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 says 'Use this to inspect a session before deciding to validate, refresh, or delete it.' This gives clear context for when to use. It doesn't explicitly exclude scenarios, but the guidance is strong.

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 exist, so the description carries full burden. It discloses that the tool uses the API key for identification and returns specific data points. While read-only nature is implied, it could be explicitly stated. Overall, it provides good behavioral context.

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?

Three sentences: purpose, use cases, and a note on parameters/authentication. Every sentence adds value; no redundancy. Front-loaded with the core action and resource.

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?

Despite no output schema, the description gives a solid sense of return values (credits by time periods, top domains) and authentication. Could be slightly more explicit about output format or data freshness, but it is sufficiently complete for a simple 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?

Schema has 0 parameters, and the description confirms 'No parameters required' and explains authentication via API key. This adds value beyond the empty schema, making it clear that no input is needed.

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 states a specific verb ('Get') and resource ('detailed spending breakdown for your AlterLab account'), listing exact outputs (credits today/week/month, top domains). It clearly distinguishes from siblings, which are about scraping, crawling, batch operations, etc.

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 cases ('audit costs, identify expensive domains, track usage trends') and notes 'No parameters required'. However, it does not provide explicit exclusions or mention alternative tools, though context from sibling names makes this less critical.

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, description transparently states it lists features and opt-in state, implying read-only behavior. Does not disclose performance or rate limits, but sufficient for safe use.

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?

Two concise sentences, front-loaded with key action and result. No wasted words.

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 parameters and no output schema, the description fully covers what the tool does and its output (list with opt-in state). Refers to sibling for next action.

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?

No parameters exist; schema coverage is 100%. Description adds nothing about parameters, but baseline 4 is appropriate for zero-parameter tools.

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 public beta and GA features with opt-in state, using specific verb 'list' and resource 'features'. It differentiates from sibling 'alterlab_enable_beta_feature' by mentioning that tool for opting in.

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 clear context: use to view features and opt-in state, and suggests 'alterlab_enable_beta_feature' for opting in. Does not explicitly contrast with 'alterlab_list_my_beta_features', but overall guidance is strong.

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; description reveals it is a read operation returning a compact slug list. Notes that list includes GA features and opted-in beta features. Lacks details on pagination or format but sufficient for a simple list.

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?

Three sentences, each with clear purpose: action, content, usage guidance. No redundant words; highly efficient.

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 parameterless, no-output-schema tool, description fully covers what it does, what it returns, and why to use it. Context with sibling tools is sufficient.

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?

No parameters; schema coverage is 100% trivially. Description adds meaning by explaining the composition of the list (GA + opted-in beta features), which is beyond schema structure.

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 clearly states verb 'list' and resource 'beta and GA features active on your account'. It differentiates from sibling 'alterlab_list_beta_features' by specifying scope is only active features on the user's account.

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 says when to use: 'to verify which features are available before making API calls that require them'. Does not explicitly name alternatives but context implies use sibling for full list.

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 the full burden. It clearly describes the read-only listing behavior ('List all stored sessions') and adds context about authenticated scraping. No side effects are mentioned, which is appropriate for a listing 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?

Two sentences, front-loaded with verb and resource, no filler. Every word adds value.

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 parameters and no output schema, the description is complete enough. It explains the purpose of sessions (cookies, login) and the workflow with alterlab_scrape. A minor gap: no mention of return format or pagination, but for a list tool this is sufficient.

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 no parameters (0 params, 100% coverage). The description adds no parameter info, which is acceptable since none exist. Baseline 4 for zero parameters.

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 'List all stored sessions for authenticated scraping' with a specific verb and resource. It distinguishes from sibling tools like alterlab_create_session, alterlab_delete_session, and alterlab_get_session.

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 when to use: to obtain session_id for alterlab_scrape. Implicitly excludes other operations (create, delete, get) via sibling names. Could add explicit when-not or alternatives.

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 fully carries the burden of disclosing behavioral traits. It states the tool is lightweight, non-scraping, non-rendering, costs $0.001 per call, and returns a flat list with source and depth. It also reveals features like search ranking, metadata fetching, and sitemap path handling, providing comprehensive transparency.

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 paragraph of eight sentences, packed with information. It front-loads the primary purpose and then details options. While not overly verbose, it could be more structured (e.g., bullets) for easier scanning. Nonetheless, every sentence adds value, earning a 4.

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 11 parameters and no output schema, the description thoroughly explains the tool's behavior, output format ('Returns a flat list of URLs with source and depth'), and parameter usage. It also covers edge cases like non-standard sitemap paths and suggests usage in conjunction with other tools. This makes it complete for an AI agent's decision-making.

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 the baseline is 3. The description adds value by explaining usage patterns for key parameters: 'Use include_patterns/exclude_patterns to scope discovery', 'Use search to rank URLs', 'Use include_metadata=true to also fetch page titles', and 'Use sitemap_path to specify a non-standard sitemap location.' This goes beyond the schema descriptions, justifying a 4.

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: 'Discover all URLs on a website, get a sitemap, or find specific pages within a large site.' It uses specific verbs and describes the resource (URLs on a site) and explicitly distinguishes from sibling tools like alterlab_crawl and alterlab_batch by advising to use this tool before 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 provides explicit use cases: 'Use this before alterlab_crawl or alterlab_batch to identify which pages to scrape.' It also clarifies what it does not do: 'no content scraping, no JS rendering.' While it doesn't explicitly state when not to use, the context and alternatives are clear, earning a 4.

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. It discloses that the session status is reset to active, and explains the effect of omitting cookies. It could mention if existing cookies are immediately invalidated or any authentication requirements, but overall it is transparent about core behaviors.

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?

Two sentences, front-loaded with the main action. Every word is purposeful, with no redundancy. Highly efficient.

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 tool with 3 parameters and no output schema, the description covers the main behavior and the optional cookie nuance. It could mention the return value or side effects, but it is sufficiently complete for an agent to understand usage.

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 clarifying that omitting cookies only resets failure counters, and that headers are updated. This provides context 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 the verb "Refresh" and the resource "session" with specific actions (rotating cookies, resetting failure counters). It distinguishes from sibling tools like alterlab_update_session by specifying the rotation and reset behavior, and by labeling it as the preferred way to update cookies after re-authentication.

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 a clear use case: "after re-authenticating in your browser." It also explains the behavior when cookies are omitted. However, it does not explicitly state when not to use this tool or mention alternative tools for other scenarios.

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 responsibility for disclosing behavior. It details intelligent anti-bot escalation, cost mechanics, caching policies, and various parameter effects. However, it does not explicitly state that the tool is read-only or describe potential side effects (e.g., no destructive operations), which would have earned a 5.

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 excessively long (over 1000 words) and contains a dense list of parameter usage examples that could be more succinct. While the first sentence is effective, the rest is verbose and mixes crucial behavioral info with parameter-level details, reducing 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?

Despite the complexity (37 parameters, nested objects, no output schema), the description covers essential behavioral aspects (caching, cost, authentication, geo-targeting, error escalation). It lacks an explicit description of the output format beyond 'markdown by default', but mentions alternatives. Minor gaps: no error handling details or response structure beyond formats.

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%, so the baseline is 3. The description adds minor extra context like pricing ('starts at $0.0001/page') and high-level feature summaries, but largely repeats or elaborates on what is already in the schema. It does not significantly enhance understanding of parameter semantics beyond the 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 verb ('Get data from any website', 'scrape', 'fetch') and resource ('any website', 'JavaScript-rendered pages', 'dynamic single-page apps'). It distinguishes itself from siblings by emphasizing anti-bot bypass and JavaScript rendering capabilities, which are unique among the listed tools.

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 does not provide explicit guidance on when to use alterlab_scrape versus alternatives like alterlab_crawl, alterlab_extract, or alterlab_screenshot. There is no 'use when' or 'consider using X instead' language, leaving the agent to infer usage from the lengthy parameter list.

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?

Describes returning a full-page PNG screenshot via a real headless browser and working on anti-bot sites. No annotations provided, so description carries the burden; it covers key behavioral traits but could mention performance or cost.

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?

Four sentences, each providing essential information: purpose, anti-bot capability, output type, and parameter usage. No wasted words; front-loaded with main action.

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, description adequately explains return value (full-page PNG in conversation). All parameters are described in schema and elaborated in text. Context is sufficient for agent usage.

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. Description adds usage context (e.g., 'Use wait_for to wait for a specific element') that goes beyond schema definitions, providing practical guidance.

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 clearly states the tool takes a screenshot of any website, captures a webpage as an image, or snapshots a URL visually. It distinguishes from siblings by mentioning anti-bot bypass capability and similarity to alterlab_scrape.

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 guidance on using wait_for and wait_until parameters. Implicitly distinguishes from alterlab_scrape for visual capture vs. data extraction, but lacks explicit when-not or alternatives.

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 must cover behavioral traits. It discloses cost ($0.001 per query), multi-engine failover, anti-bot bypass on scrape, and billing for optional scraping. It does not mention rate limits or caching, but the disclosed information is substantial and free of contradictions.

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 dense paragraph that efficiently conveys purpose, behavior, cost, and parameter usage. It is front-loaded with the core function. While it could benefit from bullet points for readability, it remains concise without redundant 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 11 parameters, no output schema, and no annotations, the description is quite complete. It explains the search result contents, cost, optional scraping, and key parameters. It could briefly mention pagination (page parameter behavior) but that is covered in the schema. Overall, it provides sufficient context for effective use.

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 meaning by explaining domain as 'site: operator', time_range for recency, safe_search for adult content filtering, and scrape_results for full page scraping. This exceeds the baseline by providing practical context.

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: 'Search the web, find information online, look up any topic, or research a subject across the internet.' It specifies the return format (URLs, titles, snippets) and distinguishes itself from sibling tools like alterlab_scrape by focusing on search rather than scraping a specific page.

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 guidance on when to use this tool by detailing its capabilities (multi-engine failover, cost, optional scraping). It explains parameter usage (domain, time_range, safe_search) but does not explicitly exclude use cases better served by sibling tools (e.g., alterlab_scrape for known URLs). However, the context makes the typical use case clear.

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 are provided, so the description carries full burden. It discloses that cookies and headers replace ALL existing values (not merged), and mentions rotating credentials. It could be improved by explicitly stating the session must exist, but that is implied by the required session_id.

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 concise (4 sentences) and front-loaded: first sentence states purpose, then lists properties, then highlights key behavior, and ends with 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.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a mutation tool with no output schema, the description covers usage, parameter behavior (including replacement) and when to use vs alternatives. It does not explain return values or error cases, but those are less critical for this tool. Overall, it is sufficiently 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 coverage is 100% with descriptions for all parameters. The description adds critical context beyond schema, such as 'replaces ALL existing cookies' and provides an example for the cookies parameter. This clarifies the non-merge behavior, adding significant value.

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 updates a session's properties and lists specific changeable aspects (name, cookies, headers, expiration, notes). It also distinguishes from the sibling tool by suggesting this over delete+recreate for credential rotation.

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 guidance on when to use the tool (to update properties, rotate credentials) and contrasts it with the alternative of delete+recreate. It also warns about replacement behavior for cookies and headers.

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?

Discloses that it checks session activity and cookie validity, returns status and reason if invalid. No annotations provided, so description carries burden; no side effects mentioned but validation is inherently read-only.

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?

Two sentences, front-loaded with the main action, no wasted words.

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?

Adequately covers purpose, usage context, and return information for a simple validation tool with one parameter and no output schema.

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?

Only one parameter (session_id) with 100% schema description coverage. The description does not add meaningful detail beyond what the schema already provides (UUID for session). Baseline score of 3 applies.

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?

Clearly states validation of session activity and cookie validity. Distinguishes from sibling tools like create, delete, refresh, get by focusing solely on checking status.

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 says 'Run this before scraping if you suspect a session may have expired,' providing clear context for use. No explicit exclusions but implied.

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