Salesforce

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

The description transparently explains the tool's behavior: it picks the right tool, fills arguments, and returns the result. Since no annotations are provided, the description carries the full burden and does so well. However, it could mention potential limitations, such as latency or dependency on other tools, for a perfect score.

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, with three sentences that each add value. It front-loads the core purpose and includes examples for clarity. No unnecessary words or 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?

Given the tool's simplicity (one required parameter, no output schema), the description is sufficiently complete. It covers purpose, usage, and behavior. However, it does not mention the return format or potential errors, which would make it fully 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?

The schema has 100% description coverage for the single parameter 'question', so the baseline is 3. The description adds value by explaining that the question should be in natural language and providing examples, which goes beyond the schema's generic description. This earns 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: it accepts a plain English question and returns an answer from the best available data source. It distinguishes itself from sibling tools by emphasizing natural language input and automatic tool selection, making its purpose specific and unique.

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 clear guidance on when to use this tool: when you want to ask a question in plain English without needing to browse tools or learn schemas. It includes examples of appropriate queries, but does not explicitly mention when not to use it or suggest alternatives, which would have earned a 5.

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?

Annotations indicate readOnlyHint=true, openWorldHint=true, and destructiveHint=false, which align with the research purpose. The description adds behavioral details (classification, fan-out, comparison) but does not mention data freshness or error handling. No contradiction with annotations.

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, moderately long paragraph but every sentence adds value. It's well-structured for clarity, though some might prefer bullet points for readability. 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?

Considering the tool's complexity (multiple input types, classification, fan-out, output), the description covers core functionality well, including output format. Missing error handling or interpretation guidance, but sufficient 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%, with both parameters described. The description adds context: market accepts multiple formats, depth options with default. This enhances understanding beyond the schema, earning a score above baseline 3.

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 researches a Polymarket bet by pulling Pipeworx data, specifying inputs (slug, URL, question text) and output (evidence packet plus comparison). This distinctively positions it from siblings like validate_claim or ask_pipeworx.

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?

It explicitly lists use cases ('should I bet on X?', 'what does the data say...', 'is there edge...') and notes it's the core demo product, implying better performance. However, it lacks explicit when-not-to-use or direct comparison to sibling 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 carries the burden. It discloses return data (paired data, resource URIs) and data sources (SEC EDGAR, FDA), but does not mention auth, rate limits, or any side effects. This is adequate but not comprehensive.

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 efficiently convey purpose, constraints, data sources, and benefits. No wasted words; front-loaded with key 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, the description adequately explains return data (paired data, resource URIs) and the two parameters (type and values) are well-defined. It could be slightly more detailed about the output format, but overall 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?

The schema covers 100% of parameters, and the description adds meaning by explaining the data returned per type (revenue, net income for company; adverse events, trials for drug), which goes beyond schema field 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 tool compares 2-5 entities side by side, with specific data fields per type (company or drug). It distinguishes itself from siblings like resolve_entity by focusing on comparison.

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 (comparing entities) and notes it replaces 8-15 sequential calls, but does not explicitly state when not to use or provide alternatives. However, sibling tools like resolve_entity serve as implicit 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?

The description states that it 'Returns the most relevant tools with names and descriptions,' which adds context beyond the schema. However, no annotations are provided, so the description carries full burden. It could mention that it searches by semantic similarity or that results are ordered by relevance, but it's still clear about the behavior.

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, each providing distinct value: what it does, what it returns, and when to use it. No fluff or redundancy. It is front-loaded with the core 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?

Given the tool's simplicity (2 parameters, no output schema, no nested objects), the description is complete. It covers purpose, return value, and usage context. No output schema exists, so return format isn't expected. The description does not need to elaborate further.

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 100% coverage for both parameters ('query' and 'limit'). The description does not need to re-explain them, but it adds context by giving example queries ('analyze housing market trends') and default/max limits. This provides additional meaning 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 uses specific verbs ('Search', 'Returns') and a clear resource ('Pipeworx tool catalog'). It explicitly states the tool's purpose: finding relevant tools by describing needs. This distinguishes it from sibling tools like 'ask_pipeworx' (which is for general questions) and tool-specific tools like 'sf_create_record'.

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 includes explicit guidance: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This tells the agent when to use the tool (first, when many tools are available) and implies it's not for other tasks. No alternatives are listed, but the 'FIRST' directive is strong enough to guide usage.

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 burden of behavioral disclosure. It states the tool consolidates 10–15 calls and returns citation URIs, implying it is a read-only aggregation. It does not explicitly declare read-only or discuss rate limits, but the behavior is adequately inferred.

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 extremely concise with three well-structured sentences: purpose, contents, and exclusion/alternative. No redundant phrases.

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 complexity (multiple data sources), the description is thorough: covers input format, output format (pipeworx:// URIs), and bundling benefit. With no output schema, the description provides sufficient return-value context.

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?

Input schema coverage is 100%, and the description adds value beyond the schema: it explains that names are unsupported and directs users to resolve_entity, clarifies the value parameter accepts ticker or CIK, and the type parameter is currently limited to company.

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: returning a full profile of an entity across Pipeworx packs in one call, with specific data sources listed. It distinguishes itself from sequential calls and references the alternative usa_recipient_profile for federal contracts.

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 (broad entity profile) and when-not-to-use (federal contracts, name-based queries) guidance, including a concrete alternative (usa_recipient_profile) and prerequisite action (resolve_entity for names).

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 correctly indicates a destructive operation ('Delete'), but lacks details on whether deletion is permanent, reversible, or what happens if the key doesn't exist. It adds some value beyond the schema but is minimal.

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 that front-loads the action and resource. It is appropriately sized for a simple tool.

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 simplicity (one parameter, no output schema), the description is adequate but could mention whether deletion is idempotent or errors on missing keys. It is minimally 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%, so the description does not need to add much. It mentions 'by key' which aligns with the schema's description. The description adds no extra meaning 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 action ('Delete'), the target resource ('stored memory'), and the parameter ('by key'). It distinguishes itself from sibling tools like 'remember' (store) and 'recall' (retrieve).

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 a memory needs to be removed, but provides no guidance on when not to use it or alternatives. Sibling tools 'remember' and 'recall' are obvious alternatives, but they are not mentioned.

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 discloses rate limiting (5 messages per identifier per day) and a specific behavioral rule (no verbatim prompts). This provides useful guidance beyond the input schema.

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: two sentences plus a brief note on rate limiting. It is front-loaded with the purpose and uses efficient language 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?

Given the tool's nature (feedback) and the absence of an output schema, the description adequately covers input parameters and usage context. It does not describe the return value, but that is not critical for 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?

Schema description coverage is 100%, but the description adds value by explaining the enum values in detail (e.g., 'bug = something broke...') and describing the optional context field. This enriches the meaning beyond the schema alone.

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 is for sending feedback to the Pipeworx team and lists specific use cases (bug reports, feature requests, etc.), which distinguishes it from sibling tools that serve different purposes.

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 mentions when to use the tool (for feedback) and includes important constraints like rate limiting and the instruction not to include end-user prompts. It does not explicitly state when not to use it or list 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?

Beyond annotations (readOnlyHint, openWorldHint), the description details that it walks child markets, groups related markets, checks monotonicity, and returns ranked opportunities with trade direction and reasoning. No contradiction with annotations.

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 (3 sentences) yet packs all necessary information: purpose, two modes, and return value. Well-structured with clear demarcation of modes.

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 explicitly states the return value (ranked opportunities with suggestions). All aspects of the tool's behavior and context are adequately covered.

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%; the description adds significant meaning by explaining the two modes and providing example values (e.g., 'when-will-bitcoin-hit-150k'), clarifying usage 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 tool finds arbitrage opportunities by checking monotonicity violations on Polymarket, and distinguishes two specific modes (event and topic), making it distinct from sibling 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?

Explicitly describes when to use each mode with concrete examples (e.g., single event slug vs. cross-event topic search), and explains why topic mode is needed for cases where Polymarket splits cutoffs into separate events.

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?

Beyond the annotations (readOnlyHint, destructiveHint), the description details the algorithm: scanning top markets, grouping by asset, fetching price history once, computing model probability, ranking by |edge|, and returning top N with trade direction. This provides rich behavioral insight.

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 somewhat lengthy but well-structured and front-loaded with the main purpose. Every sentence adds value, though 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 the tool's read-only nature, three well-described parameters, and no output schema, the description covers algorithm, inputs, purpose, and return content (top N with trade direction). It is complete for agent understanding.

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 100% coverage for the three parameters, and the description does not add significant new semantic detail beyond what the schema provides. 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?

The description clearly states the verb (scan/return) and resource (Polymarket markets where Pipeworx data disagrees with market price). It differentiates from siblings like polymarket_arbitrage and bet_research by specifying its unique focus on edge discovery for betting 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 explicitly states it is built for the 'what should I bet on today' question, providing clear usage context. It does not explicitly exclude alternatives or state when not to use it, but the context is strong enough to guide an agent.

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 clearly states that omitting key lists all memories, which is a key behavioral trait. However, it does not mention if the operation is read-only, or any other side effects, but given the tool's nature, this 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 two sentences, front-loaded with the action, and no unnecessary words. Every sentence provides essential 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 the tool is simple (1 optional param, no output schema, no nested objects), the description is complete enough. It explains both modes of operation. However, it could mention that the retrieved memory is returned in some format, but without an output schema, the description adequately covers the 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?

Schema description coverage is 100% (the only parameter 'key' is described in the schema). The description adds value by explaining the behavior when key is omitted, which goes beyond the schema's 'omit to list all keys' note. However, the schema already covers the basic semantics, so baseline is 3.

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 retrieves a memory by key or lists all memories when key is omitted. It distinguishes itself by describing the behavior with and without the key parameter, which differentiates it from sibling tools like 'forget' or 'remember'.

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 says to use this tool to retrieve context saved earlier, which implies when to use it, but does not explicitly state when not to use it or provide alternatives among siblings. No guidance on when to use 'list all' versus 'retrieve by key' is given beyond the schema.

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 parallel fan-out to three data sources, accepted since formats, and output shape. It does not mention error handling or pagination, but covers the core behavior well.

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 covering purpose, behavior, parameters, and output. It is front-loaded with the main purpose and uses 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 3 required parameters, no output schema, and moderate complexity, the description fully explains input formats, constraints (type=company only), output structure (changes, count, URIs), and the parallel backend fan-out. It is complete for effective tool selection and 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 has 100% coverage, but the description adds significant value: example ISO dates, relative time formats ('7d','30d', etc.), recommendation for typical monitoring ('30d' or '1m'), and alternatives for value (ticker or CIK). This enriches 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 'recent changes' and resource 'entity', and specifies the action as getting what's new since a point in time. It distinguishes from siblings like entity_profile and compare_entities by focusing on change monitoring and 'brief me' workflows.

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 tells when to use: 'brief me on what happened with X' or change-monitoring workflows. However, it does not explicitly mention when not to use or name alternatives, though siblings imply other use cases.

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 the burden. It discloses that data persists per session, with different retention for authenticated (persistent) vs anonymous (24 hours). It does not mention overwrite behavior, size limits, or any destructive actions, but the description is truthful and adds useful context beyond the schema.

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: first states action, second gives usage guidance, third discloses persistence behavior. No wasted words, information is front-loaded, and each sentence serves a distinct 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?

Given the simple key-value store (no output schema, no nested objects), the description covers purpose, usage, and retention. It lacks details on overwriting or retrieving, but those are covered by siblings 'recall' and 'forget'. For a straightforward tool, this is complete enough.

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 good descriptions for both 'key' (usage examples) and 'value' (type and examples). The description adds a general purpose but does not explain semantics beyond what the schema provides. 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?

The description clearly states the action: 'Store a key-value pair in your session memory.' It identifies the resource as 'session memory' and specifies the use case: saving findings, preferences, or context. This distinguishes it from siblings like 'recall' (retrieve) and 'forget' (delete).

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 says 'Use this to save intermediate findings, user preferences, or context across tool calls.' It also notes persistence differences for authenticated vs anonymous users, providing context for when the tool is appropriate. However, it does not explicitly state when NOT to use it or mention alternatives among 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?

No annotations. Description discloses output fields and accepted input formats. Lacks info on error handling, rate limits, or side effects. Adequate but not thorough.

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, no fluff. Front-loaded with purpose. Could be slightly more structured but very 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?

No output schema, but description lists return fields (ticker, CIK, name, URIs). Sufficient for a straightforward tool. Would benefit from more structure but is reasonable.

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%; description adds examples but does not significantly extend schema info. 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?

Clearly states it resolves an entity (company) to canonical IDs, gives examples and output. Unambiguous and distinct from sibling tools which are database CRUD or general utilities.

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 it replaces 2-3 lookup calls, implying when to use. Does not mention when not to use or alternatives, but the context makes it clear this is for entity resolution.

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?

Annotations are empty, so the description must disclose behavioral traits. The description indicates a write operation, but does not mention side effects (e.g., triggers, required permissions) or return values. It is minimally acceptable but lacks depth.

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?

Single sentence, no wasted words. Perfectly concise.

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, the description should explain what is returned. It does not. For a simple creation tool, the description is adequate but lacks detail on success/failure indicators.

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%, with both 'object' and 'fields' described adequately in the schema. The description does not add extra parameter info, but the schema already provides sufficient meaning for the agent.

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 'Create a new Salesforce record' uses a specific verb 'Create' and resource 'Salesforce record', which clearly states its action. However, it does not distinguish itself from sibling tools like 'sf_update_record' or 'sf_delete_record', but the verb alone is sufficient to differentiate.

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 on when to use this tool versus alternatives such as 'sf_update_record' or 'sf_get_record'. The description does not mention prerequisites, context, or when not to use it.

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 disclose behavioral traits. It does not indicate that deletion is irreversible, require confirmation, or specify what happens to related data. The description merely repeats the tool's purpose without adding 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?

The description is a single, concise sentence that directly states the tool's purpose. No unnecessary words or information are included.

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 performs a destructive action (delete), the description lacks important context such as irreversibility, permission requirements, or return value behavior. The presence of sibling tools also suggests a need for clearer differentiation, which is absent.

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 both parameters (id and object) with descriptions, so schema coverage is 100%. The description does not add further meaning beyond what the schema provides, earning a baseline score of 3.

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 'Delete' and the resource 'Salesforce record', and the input schema confirms it requires an object type and record ID. It distinguishes itself from sibling tools like 'sf_create_record' and 'sf_update_record' by its delete action.

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 alternatives. For example, it does not mention that 'sf_update_record' or 'sf_get_record' might be more appropriate for non-deletion tasks, nor does it warn about the irreversible nature of deletes.

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 the burden. It describes what the tool returns ('fields, relationships, metadata') but does not disclose behavior like read-only nature, authentication needs, or whether it makes API calls. However, the read-only intent is clear from 'Describe'.

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?

Single sentence, no wasted words. Front-loaded with verb and resource, followed by specifics. 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?

Given a simple input schema (1 param, no output schema) and the context of sibling tools, the description adequately explains the tool's purpose. It could mention that the output is a schema description, but the term 'Describe' implies this. No major 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?

Schema coverage is 100% with one parameter 'object' having description 'SObject type (e.g., "Account")'. The description adds 'fields, relationships, metadata' as output context but doesn't add new meaning to the parameter itself. 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?

The description clearly states the verb 'Describe' and the resource 'Salesforce SObject schema' along with specific elements like 'fields, relationships, metadata'. This distinguishes it from siblings like sf_list_objects (which lists objects) and sf_query (which queries records).

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 use when needing schema information, but lacks explicit when-to-use vs alternatives. Given siblings like sf_list_objects and sf_query, it would benefit from mentioning that this is for schema discovery, not data querying.

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?

Annotations are empty, so the description must fully convey behavior. It does not disclose whether this is a read-only operation (though implied by 'Get'), any rate limits, or what happens if the record is not found. It lacks details on error responses or field format expectations.

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?

A single, clear sentence that conveys the core purpose without unnecessary words. Every word is essential.

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 and simple parameters, the description is adequate but not thorough. It explains the primary action but omits details like return format (e.g., full record or specified fields) and error handling. For a simple get-by-ID tool, this is minimally 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% (all parameters described), so baseline is 3. The description does not add semantic value beyond the schema descriptions; it merely repeats 'object type' and 'ID'. No examples or formatting guidance are provided for the optional 'fields' 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?

The description clearly states the action (Get), resource (a single Salesforce record), and identification method (by object type and ID). It distinguishes from siblings like sf_query, sf_search, and sf_list_objects, which return multiple records.

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 on when to use this tool vs alternatives like sf_query or sf_search. For example, sf_get_record is for retrieving a specific record by ID, while sf_query is for custom SOQL queries. The description does not mention prerequisites (e.g., knowing the record ID) or when to use optional fields.

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 states the tool lists all SObject types, which is a read-only operation, but does not disclose whether there are any limitations (e.g., only standard objects, or performance considerations). The behavior is straightforward but lacks additional context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is a single sentence that is concise and front-loaded, containing no unnecessary words. Every word earns its place.

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 no parameters, no output schema, and simple behavior, the description is largely complete. It explains the tool lists all SObject types, which is sufficient for an agent to invoke it. Minor omission: not stating that the output is a list of object names, but since no output schema exists, the description adequately covers the return type implicitly.

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 and schema description coverage is 100%, so the schema fully defines the parameter space. The description adds no parameter-specific meaning, which is acceptable since there are no parameters to document. A baseline of 3 applies, but the description's clarity about listing 'all' types adds marginal 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 lists all available Salesforce SObject types in the org, using a specific verb (list) and resource (SObject types). It is distinct from sibling tools like sf_create_record or sf_query, which perform different operations.

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 explicitly state when to use this tool versus alternatives, but since it has no parameters and is a simple listing, usage is implied as a first step before using other Salesforce tools. No exclusion criteria or context is provided.

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 does not disclose read-only behavior, that it only reads data (inferred from 'query'), nor any potential errors or side effects. It lacks detail on return format (e.g., single record vs list) or pagination.

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 short, clear sentences with no fluff. All information is 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?

Given the single parameter and lack of output schema, the description is adequate but minimal. It does not mention how results are returned (list, single object) or any query limitations.

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 a clear description for the 'query' parameter including an example. The description adds no additional meaning 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 tool executes a SOQL query against Salesforce and returns matching records. It distinguishes itself from sibling tools like sf_search (likely a different search type) and sf_describe (metadata retrieval).

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 on when to use this tool vs alternatives like sf_search or sf_get_record. No mention of limitations (e.g., query row limits, governor limits).

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 description must disclose behaviors. It does not mention any side effects, required permissions, rate limits, or return format. 'Search' implies read-only, but without explicit statement, transparency is low.

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?

Single sentence, no filler. Every word is necessary 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?

Given no output schema and no annotations, the description is minimal but functional. It explains input but not output or behavior. For a single-param tool, it's adequate but not thorough.

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 provides a concrete example of the SOSL search string, which adds significant meaning beyond the schema's generic description. This helps the agent understand the expected format.

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 executes a SOSL search across Salesforce objects, which distinguishes it from sf_query (SOQL). The verb 'search' and resource 'Salesforce objects' are specific, though it doesn't explicitly contrast with sf_query.

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 use for SOSL searches, and context signals show sibling sf_query for SOQL, so usage is implied. However, no explicit guidance on when to use SOSL vs SOQL or when not to use this 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?

Annotations are empty, so description carries full burden. It does not disclose that updates are destructive (overwrites fields), whether partial updates are supported, or if any fields are immutable. The behavior is implied but not detailed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

Single sentence is efficient and front-loaded. No wasted words, but could be slightly more specific without being 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 the tool's simplicity (3 required params) and lack of output schema, the description is too brief. It lacks context about partial updates, error handling, or field validation, which are important for an update operation.

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 each parameter has a description. The tool description adds no extra meaning beyond the schema. 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?

The description clearly states the tool's action ('Update') and resource ('existing Salesforce record'). It distinguishes from sibling tools like sf_create_record and sf_delete_record by specifying update, but could be more precise by noting it updates by ID.

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 on when to use this tool versus alternatives like sf_get_record (to retrieve before update) or sf_describe (to check field validity). The description does not mention prerequisites such as needing the record ID or field names.

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 the return values (verdict, extracted structured form, actual value with citation, percent delta) and the supported claim types. It does not mention any side effects or destructive behavior, but as a read-only tool, that 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 concise, with three sentences. The first sentence states the main purpose, and subsequent sentences add important details about scope, output, and efficiency benefits. No unnecessary 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 the single parameter and no output schema, the description fully covers the tool's functionality, including supported claim types, output components, and the sources used. It is complete for an agent to select and invoke the 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?

The schema already provides 100% coverage for the single parameter 'claim' with a description and examples. The tool description adds marginal value by reiterating that it is a natural-language claim. 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?

The description clearly states the tool's purpose: 'Fact-check a natural-language claim against authoritative sources.' It specifies the domain (company-financial claims for US public companies), the sources (SEC EDGAR + XBRL), and the output. This distinguishes it from sibling tools, none of which are for fact-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?

The description explains when to use the tool (instead of 4-6 sequential agent calls) and provides examples of valid claims. However, it does not explicitly state when not to use it or mention alternatives, though the context is clear.

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