Onchain Alpha Lab

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

No annotations provided, so the description must carry the burden. It fails to disclose behavioral traits such as whether it makes network calls, requires permissions, has side effects, or any rate limits. It only lists signal categories.

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 that is front-loaded with the action and lists key outputs. It is concise but could benefit from breaking into parameters and usage.

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

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

With 4 parameters, 0 required, no annotations, and only a high-level description, the tool definition is incomplete for reliable selection and invocation. It lacks detail on how to use parameters and what the output contains.

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%, so the description should compensate. It only hints at 'token, contract, or candidate token lists' corresponding to token_address and candidates, but does not mention chain or focus, nor explain their roles.

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 analyzes tokens, contracts, or candidate lists using multiple signal types (market, liquidity, activity, etc.), distinguishing it from sibling tools like analyze_x_content (social content) and distill_kol_history (KOL history).

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. The description does not mention prerequisites, when to prefer it over siblings, or any 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 provided, the description bears full responsibility for behavioral disclosure. It only states 'Analyze', implying a read-only operation, but does not explicitly confirm no side effects, no data modification, or any required authentication. This lack of transparency limits the agent's understanding of the tool's safety profile.

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 18-word sentence that is front-loaded with the key action and resource. Every word adds value, with no redundancy or fluff. It is optimally 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?

Given the moderate complexity (5 optional parameters, output schema exists), the description covers both input scope and expected output types (summary, sentiment, narrative, spread risk, trading clues). It does not explain optionality or parameter combinations, but the output schema provides additional detail. Slightly more depth would make it 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 description adds meaning by listing input types 'text, accounts, topics, or content collections', which map to the parameters content, account, topic, and items. However, it does not cover the 'language' parameter, and schema descriptions are absent (0% coverage). The description partially compensates for the schema gap but is incomplete.

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 'Analyze' and the resource 'Twitter/X text, accounts, topics, or content collections'. It distinguishes itself from sibling tools (onchain analysis, KOL history, pool report) by specifying its domain as Twitter/X content analysis, making its purpose unmistakable.

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

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

The description implies usage for analyzing Twitter/X content to derive summary, sentiment, narrative, spread risk, and trading clues. However, it does not explicitly state when to use this tool versus alternatives like analyze_onchain_alpha or distill_kol_history, nor does it provide 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?

Without annotations, the description must fully disclose behavioral traits. It only states 'fetch and distill' but omits critical details: does it make network requests? Is there a rate limit? What does 'distill' entail? Are there authentication requirements? The description is too vague for an agent to understand side effects or risks.

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 very concise at two sentences, with the core action front-loaded. No redundant information. However, the use of 'buyer' instead of 'user' is a minor distraction.

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 parameters and no output schema explained, the description is incomplete. It does not specify what 'distill' produces (even if output schema exists, the process matters), prerequisites (e.g., user must exist), or potential errors. The tool's complexity demands more context for safe and effective use.

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

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

With 0% schema description coverage, the description must explain all parameters. It mentions 'username' and 'distillation direction' but fails to define what 'direction' means (e.g., chronological, thematic?). The 'max_tweets' parameter is not mentioned at all. This leaves significant ambiguity 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?

Description clearly states 'Fetch and distill historical original tweets for a specified Twitter/X user', providing a specific verb ('fetch and distill') and resource (historical original tweets of a user). It distinguishes from sibling tools like 'analyze_onchain_alpha' (on-chain focus) and 'analyze_x_content' (general X content analysis) by targeting a single user's historical tweets.

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

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

Description lacks any guidance on when to use this tool versus alternatives. It does not mention prerequisites, limitations, or comparison with sibling tools like 'analyze_x_content' which might also handle tweet data. The usage context is only implied through the tool's purpose.

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 hints at data sources (K-line, holders, etc.) and conditions ('when available') but does not disclose important behavioral aspects such as rate limits, destructive potential, or what happens when data is missing. No annotations are provided to compensate.

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 with no redundancy, efficiently stating the core purpose. However, it lacks structure and front-loading of critical usage details.

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 having an output schema, the description provides no context about report format or scope. Given the complexity (4 parameters, no annotations), the description is insufficient for an agent to fully understand tool behavior 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?

With 0% schema description coverage, the description should explain parameters but only vaguely mentions data sources. It does not clarify the purpose of 'focus', 'time_window', or 'chain' beyond defaults. Parameter meaning remains ambiguous.

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 generates a review report for new pools or breakout tokens, listing specific data sources (K-line, holders, social mentions, etc.). This distinguishes it from sibling tools that focus on single analysis aspects.

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 comprehensive review is needed but lacks explicit when-to-use or when-not-to-use guidance, and does not 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.