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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'extract semantic memory' and a configuration requirement, but it does not disclose side effects (e.g., whether audio is stored, deleted, or processed), permissions needed, or the return format. The description is vague about the tool's operational behavior, which is a significant gap given the lack of 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: two sentences, with the main action front-loaded. The 'NEW (Audio Intelligence)' prefix is a minor label but not wasteful. Every sentence contributes either purpose or a prerequisite, making it appropriately sized without unnecessary fluff.

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 annotations, no output schema, and an incomplete parameter schema, the description must provide more context. It states the tool's purpose and a config requirement, but it does not explain return values, what 'extract semantic memory' entails, or how the parameters interact. This leaves significant gaps for an agent to correctly invoke and interpret the tool, especially with 4 parameters and a nested metadata object.

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 only 25% (only audio_url has a description), so the description must compensate for the other parameters. The description lists audio types (calls, voice notes, podcasts, music) that map to the source_type enum, but it does not explain audio_url, customer_id, or metadata fields. This offers minimal additional meaning beyond the schema, failing to fill the coverage gap.

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 with a specific verb ('Ingest audio') and resource ('calls, voice notes, podcasts, music') and an outcome ('extract semantic memory'). It does not explicitly differentiate from sibling tools like audio_recall or remember, but the term 'ingest' and 'audio-intelligence layer' imply a distinctive ingestion-focused role, which is clear enough.

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 for ingesting audio into semantic memory, and it provides a prerequisite (BLUECOLUMN_API_URL pointing at the BlueColumn API). However, it does not explicitly state when to use this tool vs alternatives (e.g., audio_recall for retrieval), or when not to use it. The guidance is mostly implied rather than explicit.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. It merely restates the purpose without explaining what 'audio memories' means, how citations are generated, whether it returns audio or text, any rate limits, or required permissions. The description adds no behavioral context beyond the tool's name.

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, which is concise, but it is severely under-specified for a tool with 4 parameters and no annotations. There is no structure, such as separate clauses for purpose and usage. The brevity here is not efficient because it sacrifices necessary 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 complexity (4 parameters, one required), no output schema, and no annotations, this description is completely inadequate. It does not explain return values, parameter semantics, or any behavioral characteristics, leaving the agent with essentially only the tool's name to infer 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?

Schema description coverage is 0% and the description does not compensate. It never mentions 'query', 'top_k', 'customer_id', or 'filter_type'. The description provides zero information about what these parameters mean, how they interact, or how to construct a valid request.

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

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

The description clearly states the tool's function: 'Search audio memories and get audio-backed citations.' It uses a specific verb ('search') and resource ('audio memories'), and mentions the output ('audio-backed citations'), which distinguishes it from general tools like 'note' or 'audio_ingest'. However, it does not explicitly differentiate from similar sibling tools like 'recall' 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?

No usage guidance is provided. The description does not indicate when to use this tool versus alternatives, mention prerequisites, or give context for which scenarios are appropriate. This leaves the agent with no basis for selecting this tool over siblings like 'recall' or 'audio_ingest'.

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 must carry the full burden of behavioral disclosure. It only says 'Store', which implies a write operation, but it does not explain side effects, idempotency, required permissions, or what happens if the call_id does not exist. This is a significant gap for a mutation tool.

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

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

The description is a single sentence and is front-loaded with the purpose. The prefix 'NEW (Audio Intelligence):' adds context but is not strictly necessary. It is concise and avoids redundancy, though it could be slightly more informative without becoming 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?

With five parameters and no output schema, the description is too minimal. It does not explain what the expected inputs are, what the return value looks like, or any preconditions. Given the tool's complexity, more detail is needed for an agent to use it 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 description coverage is 0%, and the description does not explain any of the five parameters. It says 'what changed' but does not map that to 'new_memories', 'follow_up_required', or 'sentiment_improved'. The agent must rely solely on parameter names, which is insufficient.

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: 'Store what changed after a call ends.' It uses a specific verb ('Store') and identifies the resource (what changed in a call). It distinguishes itself from siblings like 'call_prepare' by focusing on post-call completion, though it does not explicitly contrast with 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 gives clear timing context: 'after a call ends.' This implies when to use the tool. However, it does not state when not to use it or mention alternatives, which would justify a 5. The context is sufficient for basic 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It indicates a read-like operation via 'Get', but does not state whether it has side effects, requires specific permissions, or what the returned context contains. This is too minimal to be considered transparent for an unprecedentedly annotated tool.

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

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

The core description is a single, front-loaded sentence that clearly states the action and context. However, the 'NEW (Audio Intelligence)' prefix is non-informative noise that wastes tokens; without it, this would be a perfect 5.

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 no annotations and no output schema, this description is incomplete. It provides purpose and timing but omits parameter behavior, expected output, prerequisites, and any interaction with sibling tools, leaving significant gaps for an agent trying to invoke it 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 description coverage is 0%, and the description does not mention customer_id or include at all. While the schema's enum values hint at the include options, the description adds no meaning to the parameters, forcing the agent to infer how to use them from names 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 uses a specific verb 'Get' and resource 'memory context', with a clear temporal scope 'before a voice call starts' that distinguishes it from siblings like call_complete and audio_recall. This directly states the tool's function and intended point in the call lifecycle.

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 phrase 'before a voice call starts' implies the usage timing, but it does not explicitly mention alternatives or exclusions. No guidance is given on when not to use this tool or how it compares to sibling tools like recall or audio_ingest, so the usage context is present but not fully developed.

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 indicates extraction/analysis but does not disclose whether it is read-only, what the output format looks like, or any side effects. The behavioral transparency is minimal beyond the implied analysis nature.

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, focused sentence with no wasted words. It front-loads the 'NEW (Audio Intelligence)' label and clearly states the tool's 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 schema and lack of annotations, the description is incomplete. It fails to cover all available extraction options, mislabels one, does not explain the audio_id parameter, and does not clarify whether the extract field is optional or what the default behavior is.

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 0% description coverage, so the description must compensate. It mentions some enum values (tempo, structure, instrumentation) but omits others (energy_curve, vocal_characteristics, motifs). It also introduces 'mood' which is not in the schema's enum, potentially misleading. The required parameter audio_id is not described at all.

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 extracts tempo, structure, instrumentation, and mood from music, which is a specific verb+resource. However, it does not explicitly distinguish from the sibling tool sound_analyze, though the focus on 'music' hints at differentiation.

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 music_analyze versus sound_analyze or other alternatives. The description does not mention use cases, exclusions, or prerequisites.

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 burden of disclosing behavior. It indicates a read-only operation ('Get') and provides the default value 'nl', giving useful context. It doesn't explicitly state that there are no side effects or describe the return format, but for a simple getter 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 a single, well-structured sentence that front-loads the action and includes all essential information (purpose and default) without any 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?

This is a trivial tool with no parameters and no output schema. The description fully explains its purpose and the default value, making it complete for the agent's needs. Nothing else is required.

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 tool has zero parameters, so the baseline is 4. The description adds semantic value by explaining the default namespace value, which is 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 uses the specific verb 'Get' and identifies the resource ('BlueColumn namespace') with a default value, making the tool's function unmistakable. It clearly distinguishes itself from the sibling note/audio tools by being a configuration getter.

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?

Usage context is implied: it's a getter for the configured namespace, so an agent would use it when needing to know the write/read namespace. However, no explicit 'when to use' or alternatives are stated, and there is no exclusion guidance relative 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 provided, the description carries the full burden. It discloses that the note is stored as a searchable vector and that it is faster than 'remember' for short notes. However, it does not mention return behavior, potential failures, or limits beyond the minimum characters, leaving some behavioral gaps.

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-loads the core purpose, and follows with usage context. Every word adds value, with no repetition or fluff. 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?

For a simple two-parameter tool with no output schema, the description covers the what, when, and how it compares to a sibling tool. It does not explain the return value, but this is not critical for a store operation. It is sufficiently complete for the tool's 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 coverage is 100% as both 'text' and 'tags' have descriptions. The description adds no additional parameter-level meaning beyond the schema, earning the baseline score of 3. It does not elaborate on tag format or constraints beyond what is already present.

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 specifies the verb 'Store' and the resource 'lightweight agent observation as a searchable vector'. It distinguishes from the sibling 'remember' by noting it is for quick notes and avoids full document processing, making the purpose 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?

Explicit usage guidance is provided: 'Use when the agent wants to save a quick preference, decision, or observation without needing full document processing.' It also names the alternative 'remember' and compares performance ('Faster than remember for short notes'), clearly indicating when to use this tool versus 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?

No annotations are provided, so the description carries the transparency burden. It discloses a key behavioral trait: 'Returns an AI-synthesized answer with source citations.' It implies a read-only query operation, which is sufficient for a retrieval tool, but does not discuss auth, rate limits, or potential side effects.

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

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

Two sentences, front-loaded with the core function and output. Each sentence earns its place: the first defines the operation and result, the second lists use cases. No fluff 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?

For a simple one-parameter tool with no output schema, the description adequately covers purpose, usage, and return type ('AI-synthesized answer with source citations'). It falls short of detailing the citation structure or any failure modes, but is sufficient for this level of complexity.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds no additional meaning beyond the schema's 'Natural language query to search memory.' No examples or format details are provided.

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 action ('Query BlueColumn memory') and notes the AI-synthesized answer with citations, making the tool's purpose clear. It implicitly differentiates from siblings like 'remember' (likely write) and 'audio_recall' (audio-specific), but does not explicitly name alternatives or contrast them.

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

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

Explicitly states when to use: 'when the agent needs to retrieve past information, answer questions about stored content, or get context from previous sessions.' This is clear context, though it does not mention exclusions or alternative tools.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does disclose that the tool returns AI-extracted summaries, action items, and key topics, and implies persistence. However, it omits potential details like permission requirements, reversibility, or handling of audio transcription (which is only in the schema). This is moderate transparency for a write operation.

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-loaded with the primary action, and every sentence earns its place: purpose, return value, and usage context. No filler 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?

The description covers the tool's core function, return values, and usage scenario. Since there is no output schema, the description appropriately explains what the tool returns (summary, action items, key topics). It does not address edge cases or failure modes, but for a straightforward store-and-summarize tool, it is largely complete.

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

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

Schema description coverage is 100%, with all four parameters already described in the schema. The description only restates that it stores text, document URL, or audio URL, adding no new meaning or constraints beyond what the schema provides. Baseline of 3 is appropriate.

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

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

The description clearly states the tool stores text, document URLs, or audio URLs into persistent memory, with a specific verb 'Store' and resource 'BlueColumn persistent memory'. It also distinguishes itself from potential siblings like 'recall' by explicitly framing this as a save operation for future 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 provides explicit usage context: 'Use when the user or agent wants to save information for future recall.' It does not explicitly mention alternatives or when not to use, but the clarity of the use case is sufficient for selection among 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 full responsibility for disclosing side effects. 'Detect and index' implies state changes (indexing), but there is no mention of required permissions, reversibility, what happens to existing indices, or any rate/limit behavior. This is a significant gap for a tool that likely writes data.

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 and is concise, but it includes a 'NEW (Audio Intelligence)' label that adds no value. It is appropriately short, but the brevity sacrifices informative content, making it only minimally effective.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

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

This is a complex audio analysis tool with three parameters and no output schema or annotations. The description is only one sentence and fails to explain return values, how indexing works, or the role of each parameter, making it inadequate for reliable 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?

Schema description coverage is 0%, and the description does not explain any of the three parameters (audio_id, search_for, detect_events). The agent has no semantic guidance beyond parameter names, which is insufficient.

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: detecting and indexing non-speech audio events. It distinguishes itself from siblings like music_analyze by explicitly targeting non-speech audio.

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 explicit when-to-use or alternative guidance is provided. The description implies use for non-speech audio event detection, but it doesn't contrast with music_analyze or audio_recall, leaving the agent to infer applicability.

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