AgentData - Executable Liquidity / Exit-Cost
Server Details
Size-aware exit cost, depeg risk & liquidity fragility for a Base token (x402, testnet).
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- phenicea/agentdata
- GitHub Stars
- 0
Available Tools
1 toolliquidity_exit_costAInspect
Size-aware exit cost, depeg risk, and liquidity fragility for a token on Base. Given a token and a sell size, returns the realized cost of exiting (price impact + fee, in bps and units), the best route across venues, an aggregated fragility score, and — for pegged assets — a depeg-risk score. Computed deterministically from on-chain AMM state, so the result is reproducible and auditable. Three tiers by compute depth (default 'risk'): 'quote' = best-route exit cost for one size; 'risk' = exit cost + fragility (+ depeg for pegged tokens); 'deep' = adds a multi-size exit-cost curve, max-size-before-cost thresholds, and an internal cross-check. Pricing per call (USDC): quote = $0, risk = $0, deep = $0. Status: testnet/preview — payment runs over the x402 HTTP surface, not this tool.
| Name | Required | Description | Default |
|---|---|---|---|
| pool | No | Optional: restrict the computation to a single pool id. | |
| size | Yes | Sell size in human token units (> 0). | |
| tier | No | Pricing/compute tier. 'quote' = best-route exit cost only; 'risk' (default) adds fragility + depeg; 'deep' adds a multi-size exit-cost curve and cross-check. | risk |
| token | Yes | Token symbol/address to exit (sell), e.g. 'WETH'. |
Output Schema
| Name | Required | Description |
|---|---|---|
| size | Yes | |
| tier | Yes | |
| depeg | No | |
| route | Yes | |
| token | Yes | |
| network | Yes | testnet | mainnet |
| exit_cost | Yes | |
| fragility | No | |
| cross_check | No | |
| exit_cost_curve | No | |
| max_size_before_cost | No |
TDQS
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 covers determinism ('Computed deterministically from on-chain AMM state'), reproducibility, return content, pricing (all $0), and status ('testnet/preview') with a note that payment runs over x402, not this tool. This is comprehensive transparency for a computation 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 compact and efficient, with the primary purpose front-loaded. Each sentence adds value: outputs, computation method, tiers, pricing, and status. No unnecessary filler or repetition of schema 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?
Given the tool's complexity (4 parameters, output schema present), the description is remarkably complete. It explains the return concepts, computational basis, tier options, and operational status. The output schema handles detailed return fields, so the description's high-level output summary is sufficient. No obvious gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already has 100% parameter descriptions, so the baseline is 3. The description adds meaningful depth by explaining what each tier returns in terms of outputs (e.g., quote = best-route exit cost, risk adds fragility and depeg, deep adds a multi-size curve). This goes beyond the schema's simple enum labels and directly aids parameter selection.
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 computes size-aware exit cost, depeg risk, and liquidity fragility for a token on Base. It specifies the verb 'returns' and lists concrete outputs (price impact + fee, best route, fragility score, depeg-risk score), distinguishing it from a generic liquidity tool. No sibling tools exist to differentiate, but the scope and resource are explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use each compute tier ('quote', 'risk', 'deep') with detailed explanations of what each returns. It also notes the default tier and pricing per call. While it does not explicitly state 'use when' or name alternatives (no siblings), the tier guidance effectively tells the agent how to choose the appropriate depth for the task.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
1 tool update
- First observed
liquidity_exit_cost
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TDQS
Only one tool exists, so there is no possibility of confusion between tools. The tool's description clearly delineates its singular purpose.
With a single tool, naming consistency is trivially maintained. The name 'liquidity_exit_cost' is descriptive and follows a clear noun_noun pattern.
A single tool feels thin for a server purportedly focused on 'Executable Liquidity / Exit-Cost'. The tool itself provides multiple tiers of analysis, but the overall surface area is minimal, falling into the borderline category.
The tool covers exit cost, depeg risk, liquidity fragility, and multiple depth tiers (quote/risk/deep), addressing the core domain effectively. Minor gaps might include none obvious for its narrow scope, though it lacks separate endpoints for individual metrics.