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spectra_get_gauge_votes

Retrieve veSPECTRA gauge vote distribution, voting APRs, bribe incentives, and SPECTRA emissions across pools. Analyze governance dynamics and maximize voting returns.

Instructions

Full veSPECTRA governance dashboard: gauge vote distribution, voting APRs, bribe incentives, SPECTRA emissions per pool, and Merkl campaign health.

Shows where veSPECTRA holders direct their votes, what rewards they earn for voting, and how SPECTRA emissions are distributed across pools. Essential for:

  • Deciding where to direct veSPECTRA votes for maximum return

  • Finding pools with active bribe markets (voting incentives)

  • Understanding emission concentration and governance dynamics

  • Evaluating bribe efficiency ($ per vote)

  • Detecting broken emission pipelines (votes directing SPECTRA to pools where no Merkl campaign exists to distribute them)

Each gauge is cross-referenced against live Merkl campaigns: ✅ = Merkl campaign active at the gauge's pool address (emissions flowing) ⚠ = Merkl campaign exists but targets a different pool address (likely stale — gauge rolled to a successor pool but the campaign wasn't updated) ❌ = No campaign found (emissions allocated by governance but not distributed)

The ⚠ stale detection uses fuzzy symbol matching: if the gauge symbol (e.g., "yvvbUSDC") appears in a Merkl campaign name containing "Spectra" but the campaign targets a different address, the campaign likely belongs to a matured predecessor pool. This catches the common failure mode where pools roll to new maturities but Merkl campaigns lag behind.

The voting APR is what veSPECTRA holders earn by directing votes to a gauge: voting APR = (voting rewards + swap fees) / vote value

Bribe efficiency = total voting rewards / total votes (in $). Higher efficiency = more reward per unit of voting power directed.

Use spectra_get_ve_info for your personal boost calculation. Use spectra_list_pools to see the pools behind these gauges. Use spectra_list_expiring_pools to check gauge status for expiring pools.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sort_byNoSort gauges by: votes (total voting power), voting_apr (voter return), emissions (SPECTRA directed), bribes (voting rewards value). Default: votes.votes
top_nNoNumber of gauges to show (default 20, max 100).
chain_filterNoFilter to a specific chain ID (e.g., 1 for Ethereum, 8453 for Base). Omit for all chains.
min_votesNoMinimum votes (in SPECTRA, not wei) to include. Default 0.
Behavior4/5

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

No annotations provided, but the description explains return data concepts (voting APR formula, bribe efficiency, Merkl campaign statuses with emojis), stale detection logic, and what the tool does not do (e.g., personal boost). It lacks explicit mention of read-only nature, but overall behavior is clearly disclosed.

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

Conciseness4/5

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

Well-structured with bullet points and emojis. Front-loaded with main purpose. Every sentence adds value, though slightly verbose; could be trimmed slightly without loss of clarity.

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

Completeness5/5

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

Given complexity and no output schema, the description is very complete: explains metrics, formulas, indicators, edge cases (stale detection), and interpretations. Covers all major aspects expected from a governance dashboard tool.

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

Parameters4/5

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

Input schema has 100% description coverage. The description adds value by explaining the meaning of sort options (votes, voting_apr, emissions, bribes) and clarifying that min_votes is in SPECTRA, not wei, which goes beyond the schema.

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

Purpose5/5

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

The description clearly identifies the tool as a 'Full veSPECTRA governance dashboard' covering gauge vote distribution, voting APRs, bribes, emissions, and Merkl campaign health. It distinguishes from siblings by listing related tools like spectra_get_ve_info, spectra_list_pools, and spectra_list_expiring_pools.

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

Usage Guidelines5/5

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

Explicit guidance on when to use: deciding where to direct votes, finding pools with bribe markets, understanding emission concentration, evaluating bribe efficiency, and detecting broken emission pipelines. Sibling tools are recommended for related tasks, providing clear decision context.

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

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