Yesterdays Number

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

With no annotations, the description carries the full burden. It discloses that the tool picks the right tool and fills arguments, but doesn't detail limitations, failure modes, or behavior under ambiguity. The description is moderately transparent but leaves gaps about edge cases.

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 plus examples, which is concise and front-loaded with the core function. It efficiently conveys the tool's purpose without unnecessary detail, though the examples could be shortened.

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 a simple single-parameter schema and no output schema, the description adequately explains what the tool does and how to use it. It covers the essential aspects for a natural language query tool, though it could mention response format or expected behavior more explicitly.

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% for a single parameter. The description adds value by clarifying that the parameter should be a natural language question, and provides examples. It goes beyond the schema's 'Your question or request in natural language' by illustrating 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: answer plain English questions by automatically selecting the best data source, filling arguments, and returning results. It distinguishes itself from sibling tools by describing an agent-like capability rather than direct data retrieval or memory 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 explains when to use the tool: when you have a question in plain English and don't want to browse tools or learn schemas. It provides concrete examples. However, it doesn't explicitly state when not to use it or mention alternative tools for specific 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 discloses return data (paired data + resource URIs) and query fields per type. It does not mention failure modes, rate limits, or if the operation is read-only; however, for a comparison tool, the disclosed detail 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 extremely concise: two sentences, no fluff, front-loaded with purpose and key details. Every sentence 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 no output schema, the description fully explains return format (paired data + URIs) and covers all required inputs. It also mentions the efficiency gain vs. sequential calls, making it contextually 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%, and description adds value by explaining type-specific fields, giving examples (tickers vs. drug names), and clarifying entity constraints (2-5 items). This exceeds the baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool compares 2-5 entities in one call, with explicit differentiation by type (company vs. drug) and specific fields returned. It also contrasts with sequential agent calls, distinguishing it from siblings.

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 specifies when to use (comparing entities in bulk) and the entity count range (2-5). It implies efficiency benefits but does not explicitly list when-not-to-use or alternatives among siblings, though the context excludes common 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?

No annotations provided, so description carries full burden. It states the tool returns 'most relevant tools with names and descriptions', which is helpful but doesn't disclose details like whether results are ranked, if there is pagination, or if it only returns tools from the same server. Adequate but not exhaustive.

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

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

Two sentences, both front-loaded with key information. No redundant words. Every sentence provides value: first sentence defines purpose, second gives usage guidance.

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 (2 params, no output schema), the description is fairly complete. It explains what it does, when to use it, and the nature of the query. Minor gap: doesn't mention the return format beyond 'names and descriptions', but for a search tool that returns tool metadata, this 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. Description does not add extra parameter details beyond the schema. The schema itself includes clear descriptions for 'query' and 'limit'. The description reinforces the use of natural language for query, but doesn't add new semantics.

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 searches the tool catalog using natural language queries. The verb 'Search' and resource 'Pipeworx tool catalog' are specific. The description also differentiates from siblings by advising to call this first when there are 500+ tools, which is a unique role.

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 tells the agent to 'Call this FIRST when you have 500+ tools available', providing clear when-to-use guidance. It does not mention when not to use, but the directive to use it first implies it's for tool discovery, not for other tasks.

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, but description explains aggregate behavior, supported entity types, required identifier format, and output format (pipeworx:// URIs). Lacks rate limits or auth details, 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?

Single dense paragraph front-loads purpose, then details scope and benefits. Every sentence adds value; no waste.

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 explains return format (citation URIs). Covers current limitations and future plans. Complete for an aggregation 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 covers parameters fully (100% coverage). Description adds context: value accepts ticker or CIK, and names require prior resolution. Adds meaningful guidance beyond 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 aggregates an entity's profile across multiple Pipeworx packs, with specific data sources listed. It distinguishes from siblings by noting it replaces 15-30 sequential calls.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly advises when to use (for comprehensive profile in one call) and when not (names not supported; use resolve_entity first). Also notes only 'company' type is supported.

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 indicates deletion, which is destructive, but no annotations exist. It does not disclose whether the deletion is permanent, requires confirmation, or has any side effects. With no annotations, the description partially carries the burden 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?

Single sentence, no wasted words. Perfectly concise 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 low complexity (1 required param, no output schema), the description is adequate but could mention if the key must exist or what happens on success/failure. No output schema means description could clarify return value.

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 parameter 'key' is already described in the schema. The description adds no additional meaning beyond what the schema 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 tool deletes a stored memory by key, which is a specific verb+resource. It distinguishes from siblings 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?

No guidance is provided on when to use this tool versus alternatives (e.g., recall vs forget). The description implies usage when deleting a memory, but does not mention prerequisites or 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?

With no annotations, the description discloses the rate limit (5 messages per identifier per day) and that feedback is sent to the team. No destructive or behavioral side effects are relevant here.

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 with zero waste. Purpose first, then guidelines, then rate limit. Every sentence 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?

For a simple feedback tool with no output schema, the description covers purpose, usage, rate limit, and content constraints. Sufficient for correct agent 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?

Schema coverage is 100% with detailed parameter descriptions. The description adds little beyond summarizing the 'type' enum and optional 'context' object, but the schema already does the heavy lifting.

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 'Send feedback to the Pipeworx team' and lists specific use cases (bug reports, feature requests, missing data, praise). It distinguishes from sibling tools like ask_pipeworx or discover_tools by focusing on feedback submission.

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 tells when to use (bug reports, etc.) and provides content guidelines (describe in terms of Pipeworx tools, do not include end-user prompt verbatim). No explicit 'when not to use' but clear enough for the 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 provided, so description must convey behavior. It states retrieval of stored memories, implying a read operation. However, it does not disclose whether memories persist across sessions, what happens on missing key, or any side effects. Adequate but lacks depth for a memory 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 core action, no wasted words. Efficiently communicates both retrieval 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?

For a simple read tool with one optional parameter and no output schema, the description is mostly complete. It explains both usage modes and context. Lacking details on return format or error handling, but acceptable given tool simplicity.

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% and the single parameter 'key' is well described. The description adds value by clarifying that omitting key lists all memories, which is not in schema. This extra context elevates the 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?

Description clearly states the tool retrieves a memory by key or lists all memories when key is omitted. Verb 'retrieve' and 'list' combined with 'memory' resource is specific, and the phrase 'previously stored memory' distinguishes it from other tools like remember or forget.

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 tells when to use: 'retrieve context you saved earlier in the session or in previous sessions.' The omission of key is clearly noted for listing all memories, and no alternatives are needed as it's a distinct retrieval tool 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 provided, so the description fully covers behavioral traits: parallel fan-out to three sources, acceptable 'since' formats (ISO and relative), and return structure (structured changes, total_changes, URIs). No 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 concise (approx. 80 words) with no filler. Every sentence adds unique information: purpose, fan-out, parameter formats, return value, and use cases. 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 complexity (parallel queries, multiple data sources, parameter flexibility), the description is complete. It covers entity type constraint, parameter formats, return structure, and typical use cases. No output schema, but the returns are described adequately.

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: explains the 'type' enum (only 'company'), provides examples for 'since' formats, and clarifies 'value' as ticker or CIK. This goes beyond the schema definitions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'What's new about an entity since a given point in time.' It specifies the behavior for type='company' (fan-out to SEC EDGAR, GDELT, USPTO) and distinguishes it from sibling tools like entity_profile (static info) and compare_entities.

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 recommends usage: 'Use for "brief me on what happened with X" or change-monitoring workflows.' It provides clear context but does not explicitly state when not to use or mention alternatives, though the context is sufficient.

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 discloses key behavioral traits: persistence based on authentication status and session expiration for anonymous users. Since no annotations are provided, the description carries full burden, and it does so adequately without 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?

Three concise sentences: first states core function, second gives usage examples, third clarifies persistence. Every sentence adds value, 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 the simplicity of the tool (2 parameters, no output schema, no nested objects), the description fully covers what the agent needs: what it does, when to use it, and behavioral quirks (memory duration). No gaps remain.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already describes both parameters well (key with examples, value as free text). The description doesn't add extra meaning beyond confirming the key-value nature. With 100% schema coverage, 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 stores a key-value pair in session memory, with specific use cases (saving intermediate findings, user preferences, context). The verb 'store' and resource 'key-value pair' are specific, and it distinguishes itself from siblings like 'forget' and 'recall'.

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 (to save context across calls) and provides some guidance on persistence differences (authenticated vs anonymous). However, it doesn't explicitly state when not to use it or mention alternatives, but the sibling tools (forget, recall) imply complementary use.

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 takes the burden. It mentions returns (ticker, CIK, name, URIs) implying a read operation, but does not disclose behavior on non-existent entities, side effects, or permission requirements. Adequate but not exhaustive.

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

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

The description is highly concise at two sentences, front-loading the purpose and providing key details without unnecessary words. Every sentence 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 the tool's simplicity and no output schema, the description covers the main purpose, accepted inputs, and return values. It lacks details on edge cases but is complete enough for a 2-parameter lookup tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% coverage with descriptions for both parameters. The description adds concrete examples (e.g., 'AAPL', '0000320193') and clarifies the type enum, providing additional 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 tool resolves an entity to canonical IDs, with specific verb 'Resolve' and resource 'entity to canonical IDs'. It also distinguishes from sibling tools by noting it replaces 2-3 lookup calls.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage context for the 'company' type, including accepted inputs (ticker, CIK, name) and examples. It does not explicitly state when not to use or alternatives, but the context is sufficient for the current version.

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