32 paid x402 endpoints for crypto, Zora & on-chain analysis. 10 MCP tools. USDC on Base.
Daily curated alpha report with top picks, rug warnings, and DCA advice.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It implies a read-only, informational operation (generating a report) and indicates the report is 'daily', suggesting it's not real-time or interactive. This adds some behavioral context beyond the name, but it doesn't disclose whether there are any side effects, dependencies on recent data, or limitations. It's a minimal but adequate disclosure for a report 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, clear sentence that captures the core purpose without fluff. It is front-loaded with the key term 'Daily curated alpha report' and then specifies the contents. It's appropriately concise for a simple tool, though it could arguably be more descriptive without losing efficiency.
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 (no parameters, no output schema), the description gives a reasonable overview of what to expect: a report with top picks, rug warnings, and DCA advice. However, it doesn't specify the output format (e.g., markdown, JSON), whether the report is a summary or detailed, or how frequently it updates beyond 'daily'. For an agent deciding whether to call this tool, the content is clear but the format and breadth of the response are undefined.
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 no parameters, and the schema confirms this with 100% coverage. Since there are no parameters to describe, the description doesn't need to add parameter semantics. The baseline of 4 applies because the schema is complete and there's nothing missing.
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: a daily curated alpha report including top picks, rug warnings, and DCA advice. This is specific and uses a verb ('report') with a resource definition, distinguishing it from sibling tools that focus on specific checks or data (e.g., honeypot_check, token_price). However, it doesn't explicitly differentiate itself from siblings like new_launches, which could overlap with 'top picks' to some degree.
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 no guidance on when to use this tool versus the siblings. It doesn't mention alternatives, exclusions, or contexts where it's preferred. An agent would have to infer that this is a comprehensive summary tool, but there's no explicit routing information, which is a significant gap for a daily report tool that might overlap with other analysis tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Brutal chart analysis with technical signals including RSI, MACD, support/resistance.
| Name | Required | Description | Default |
|---|---|---|---|
| address | Yes |
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 conveys that the tool analyzes charts and returns technical signals, but it does not disclose whether the operation is read-only, what data source it uses, whether there are rate limits, or what the output format looks like.
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 core action ('brutal chart analysis') before listing the included signals. It contains no filler or redundancy, though the word 'brutal' adds tone rather than substantive 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?
With one required parameter and no output schema or annotations, the description needs to explain what the address refers to and what kind of result to expect. It does neither, leaving an agent without the critical information needed to call 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 has only one parameter, 'address', with 0% description coverage, and the tool description never mentions 'address' at all. The description does not help an agent determine whether the address should be a token contract, wallet, or pool, so it fails to compensate for the missing schema documentation.
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 identifies a specific action ('brutal chart analysis') and names concrete technical signals (RSI, MACD, support/resistance), making the tool's function reasonably clear. It is distinct enough from siblings like holder_analysis or token_price, though it never explicitly states what type of asset or address is being charted.
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 technical-signal phrasing implies the tool is meant for chart/technical analysis, but there is no explicit when-to-use guidance or mention of alternatives. An agent must infer the intended use case rather than being told when to choose this tool over a sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Look up a Zora creator's profile and all their launched coins.
| Name | Required | Description | Default |
|---|---|---|---|
| address | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. 'Look up' implies a read operation, but nothing is disclosed about authentication, rate limits, failure modes, or response behavior. The description stops at purpose and lacks behavioral detail.
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, front-loaded sentence conveys the purpose with zero wasted words. It is appropriately concise for a simple lookup 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?
There is no output schema or annotations, and the description only hints at the return content (profile and launched coins). It is complete enough for a straightforward query, but an agent might need details on response format, pagination, or error handling for full success.
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 must compensate for the single 'address' parameter. The wording implies the address is the creator's address, but it never explicitly says so. For a one-parameter tool this is minimally adequate, though it could be more explicit.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('look up') and resource ('Zora creator's profile and all their launched coins'), clearly distinguishing it from siblings like token_price or holder_analysis. The scope is exact and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given about when to use this tool versus the sibling tools. There are no prerequisites, exclusions, or alternative routes mentioned, leaving the agent to infer usage from purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Analyze token holder concentration with Gini coefficient.
| Name | Required | Description | Default |
|---|---|---|---|
| address | Yes |
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. 'Analyze' implies a read-only operation, but the description doesn't state whether the analysis requires prior token identification, what happens with invalid addresses, or how the Gini coefficient is computed or returned. For a tool with zero annotation coverage, this is a significant gap.
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 wasted words, and the verb and resource are front-loaded. It's appropriately terse for a one-parameter tool, though it sacrifices potentially useful detail for brevity.
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 one-parameter analysis tool, the description conveys the core function. However, with no output schema and no annotations, it fails to explain what the output looks like, what the Gini coefficient range means, or how to interpret results. Adequate but minimal coverage of 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 coverage is 0%, so the description should compensate. However, there is only one parameter ('address') which is self-evident from its name. The description doesn't clarify what type of address is expected (token contract vs. wallet), which adds some ambiguity, but the single parameter is minimally sufficient.
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?
States a specific verb ('Analyze') and resource ('token holder concentration') with a defined method (Gini coefficient). The purpose is clear and distinct from siblings like honeypot_check or token_price, which serve different purposes, though no sibling differentiation is explicitly made.
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. The agent must infer usage context from the name and description alone, with no mention of prerequisites, typical use cases, or situations where a sibling tool would be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Detect if a token is a honeypot. Simulates a sell and returns whether selling is possible.
| Name | Required | Description | Default |
|---|---|---|---|
| address | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility for behavioral disclosure. It explains the simulation of a sell and the return of whether selling is possible, which is a clear behavioral trait. However, it does not discuss potential side effects, requirements (e.g., network access), or limitations, leaving some transparency 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 concise sentences with no filler. The primary action is front-loaded, and every word contributes to understanding. This is an exemplary model of brevity.
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 one parameter and no output schema, the description could be more complete. It explains the simulation and the result but does not specify the exact return type (e.g., boolean) or differentiate from zora_rug_check. It also omits any prerequisites or edge-case behavior. While adequate for a simple tool, it leaves a few important details unspecified.
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 single parameter 'address' has no description in the schema (0% coverage). The tool description does not explicitly clarify that this is a token contract address, though the tool name and context imply it. Since there is no compensation for the schema's lack of detail, the parameter semantics are under-specified.
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 ('Detect if a token is a honeypot') and specifies the method (simulates a sell). The verb and resource are specific. However, it does not differentiate from sibling tools like zora_rug_check, which might have a similar purpose. Still, the core purpose is 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?
The description provides no guidance on when to use this tool versus alternatives such as zora_rug_check or zora_scanner. It neither states conditions for use nor mentions any exclusions. An agent would have to infer when honeypot_check is appropriate, which is not ideal.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the newest token launches on Zora with market cap and volume.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral traits. The description only states that it 'gets' launches and includes market cap and volume, which implies a read-only operation. However, it does not explicitly state read-only status, potential rate limits, authentication requirements, or any side effects. The description is insufficiently transparent for a tool with zero annotation coverage.
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, short sentence with no filler or redundancy. It front-loads the core action and key details, making it easy to scan. It earns its place without wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple getter tool with one optional parameter and no output schema, the description should at least explain the return structure or any constraints. It states that launches come 'with market cap and volume' but does not describe any other output fields, ordering, or the meaning of 'newest'. Without an output schema, the agent lacks clarity on what the tool returns. The brevity leaves important context unaddressed.
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 one optional parameter, 'limit', with no description. Schema coverage is 0%, and the tool description does not mention 'limit' at all. An agent cannot infer what 'limit' controls (e.g., number of results, pagination, time range) from either the schema or the description. The description fails to compensate for the lack of schema documentation, leaving the parameter's meaning entirely 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 verb 'Get' and the resource 'newest token launches on Zora', and specifies the data fields (market cap and volume). It is specific enough to convey the tool's core function, but it does not explicitly differentiate from siblings like zora_scanner, which might also return launch information. The purpose is unambiguous and actionable.
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?
There is no guidance on when to use this tool versus alternatives. The sibling list includes other Zora-related tools (e.g., zora_scanner, zora_rug_check), but the description does not mention any conditions, exclusions, or preferences that would help an agent decide between them. The usage context is entirely absent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get current token price from CoinGecko by id, symbol, or contract address.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It usefully discloses the data source, freshness intent, and lookup flexibility, but it does not mention return currency/units, rate limits, error behavior, or output shape. This is adequate for a simple getter but not richly 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?
A single, well-structured sentence delivers the key facts with no filler. The verb, resource, source, and query modes are all front-loaded, making it easy for an agent to parse quickly.
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 one-parameter tool this is reasonably complete, but the absence of an output schema and annotations means the description should cover return format, currency, or error cases. It captures the core intent but leaves an agent guessing about the actual response shape and edge 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?
The schema provides only a bare 'query' string with 0% description coverage, so the description rightly steps in by explaining that the query can be an id, symbol, or contract address. However, it does not clarify how those identifiers are disambiguated, case sensitivity, or expected formatting, leaving some ambiguity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Get'), a clear resource ('current token price'), and a specific source ('CoinGecko'), plus the three accepted query modes. This clearly differentiates it from the sibling analysis/checking tools without needing to open the schema.
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?
Use is implied: call this when you need a current token price from CoinGecko. However, the description does not explicitly say when to prefer this tool over sibling tools or mention any exclusions, so the guidance is implicit rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get wallet holdings on Zora with PnL and allocation breakdown.
| Name | Required | Description | Default |
|---|---|---|---|
| address | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden, but it does not disclose address validation, network requirements, data freshness, or failure behavior. The verb 'Get' weakly implies a read operation, but no explicit behavioral guarantees or caveats are provided.
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?
One front-loaded sentence with no filler; it names the verb, resource, and key output dimensions immediately. 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?
Adequate for a simple single-parameter read tool: the core input and output dimensions are inferable. But with no annotations, no output schema, and no sibling comparison, an agent still lacks guidance on address format and when to use this tool instead of related Zora tools.
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 provides some meaning by tying the sole parameter to a wallet's Zora holdings and PnL. However, it does not specify the expected address format (e.g., 0x-hex vs ENS) or any constraints, so compensation for the empty schema is only partial.
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?
States a specific action ('Get wallet holdings') on a specific resource ('Zora') and names the output dimensions ('PnL and allocation breakdown'). This clearly distinguishes it from siblings like holder_analysis or zora_rug_check, which focus on token holders or safety checks rather than a single wallet's portfolio.
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 given about when to prefer this tool over its siblings or what preconditions apply. The description only implies use for wallet portfolio lookups; it never names alternatives or exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check any Zora token for rug risk. Returns a 0-100 risk score with verdict and reasoning.
| Name | Required | Description | Default |
|---|---|---|---|
| address | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description itself must carry the behavioral disclosure burden. It does disclose the primary output shape (0-100 score with verdict and reasoning), but it doesn't explain whether the check is on-chain/off-chain, heuristic-based, or what limitations or failure modes exist. This is adequate but shallow.
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 one focused sentence followed by a clear statement of the return value. Every word earns its place, and the core purpose 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?
The description is not complete enough for a tool with no annotations and no output schema. It mentions the output score, verdict, and reasoning, but leaves out tool-selection context and the meaning/format of the single required parameter. An agent would likely need to guess between this and sibling security tools.
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 must compensate for the undocumented 'address' parameter. It doesn't specify whether address refers to the token contract, a Zora-specific identifier, or what chain/format is expected. This is a meaningful ambiguity for an agent invoking the tool.
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: checking a Zora token for rug risk and returning a numeric risk score with verdict and reasoning. It is specific about the resource and output, though it doesn't explicitly differentiate from siblings like honeypot_check.
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 about when to use this tool versus alternatives such as honeypot_check, holder_analysis, or zora_scanner. The phrase 'any Zora token' is broad but doesn't state prerequisites, network assumptions, or scenarios where another sibling would be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Scan Zora for trending coins and trading signals. Returns top movers with market cap, volume, 24h change, and BUY/SELL/STRONG_BUY signals.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the behavioral burden. It states that the tool returns a set of fields and signal ratings, which is useful, but it does not disclose data freshness, whether the operation is strictly read-only, authentication needs, or how the BUY/SELL/STRONG_BUY signals are determined.
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 short sentences: one for the action and one for the return value. Everything earns its place, there is no filler, and the key output details are presented efficiently.
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 no-parameter scanner with no output schema, the description gives the essential return fields and makes the tool's purpose sufficiently clear. It would be more complete with an explicit note that it returns a list of coin objects or a statement about signal methodology, but those are minor gaps rather than blocking issues.
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 input schema is empty and the description has no parameter semantics to explain. Per rubric, 0 params earns a baseline of 4; no additional parameter context is necessary.
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 ('Scan') and identifies the resource ('Zora') plus the output ('top movers with market cap, volume, 24h change, and BUY/SELL/STRONG_BUY signals'). It clearly conveys what the tool does, though it does not explicitly contrast itself with sibling tools like token_price or new_launches.
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 given for when to use this tool instead of a sibling such as token_price, new_launches, or chart_roast. The intended usage is only implied by the word 'Scanner' and the function name, leaving the agent to infer selection criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
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Pay-per-call crypto data on Base: market pulse, prices, chain snapshot, USDC stats. x402 USDC.
914 paid market-data & token-safety endpoints — keyless x402 USDC: Base, Solana, XRPL, +8 chains
Production-grade MCP gateway delivering 8 real-time AI tools with instant x402 micropayments settled in USDC on Base Mainnet or SPL-USDC on Solana. Features Basescan contract auditing, wallet analytics, headless browser scraping, and pre-scraped oracle data feeds.
Production-grade MCP gateway delivering 8 real-time AI tools with instant x402 micropayments settled in USDC on Base Mainnet or SPL-USDC on Solana. Features Basescan contract auditing, wallet analytics, headless browser scraping, and pre-scraped oracle data feeds.
Add one secure layer between your agents and this server.
Multiple tools have overlapping purposes: honeypot_check and zora_rug_check both assess token risk, while zora_scanner, new_launches, and alien_plugg_alpha all surface trending or recommended tokens with market signals. Descriptions help somewhat, but an agent could easily pick the wrong tool for a given task.
Names are consistently snake_case and mostly noun-based, but there is no clear verb_noun pattern and the action suffixes vary: check, scanner, lookup, analysis, roast. The zora_ prefix is helpful for Zora-specific tools, but alien_plugg_alpha and chart_roast break the broader pattern.
Ten tools is well within the ideal range for a specialized crypto/Zora analytics server. Each tool covers a meaningful slice of the space without feeling padded, and none are trivial or redundant enough to remove.
The toolset covers the core Zora analysis lifecycle: discovery, pricing, risk checks, holder analysis, charting, and portfolio tracking. Minor gaps exist, such as detailed token metadata or transaction history, but agents can accomplish most analysis workflows without dead ends.